Overview:¶
The objective of this project is to
- scrape a corpus of news articles from a set of web pages,
- pre-process the corpus, and
- evaluate the performance of automated classification of these articles in a supervised learning context.
In [1]:
import urllib.request
import bs4
import matplotlib.pyplot as plt
%matplotlib inline
from matplotlib import ticker
import seaborn as sns
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction import text
import operator
import nltk
from nltk.stem.porter import PorterStemmer
from sklearn.metrics.pairwise import cosine_similarity
from IPython.display import Image
from sklearn.neighbors import KNeighborsClassifier
import numpy
import random
seed = 42
numpy.random.seed(42)
from sklearn import metrics
from sklearn.metrics import accuracy_score
from sklearn.utils import shuffle
from sklearn.model_selection import StratifiedKFold
from imblearn.under_sampling import RandomUnderSampler
from sklearn import svm
Part 1. Data Collection¶
The goal here is to collect a labelled news corpus. Tasks to be completed:
- Identify the URLs and category labels for all news articles listed on the website: http://mlg.ucd.ie/modules/COMP41680/archive/index.html
- Retrieve all web pages corresponding to these article URLs. From the web pages, extract the main body text containing the content of each news article. Save the body of each article as plain text.
- Save the category labels for all articles in a separate file.
Many ways to parse HTML pages in Python. The third-party Beautiful Soup package is useful for working with badly written HTML pages.
We can use BeautifulSoup to find all the tags we need and get the text between them.
In [2]:
link_all_months= "http://mlg.ucd.ie/modules/COMP41680/archive/"
#Fetch the HTML code from the web page
response_all_months = urllib.request.urlopen(link_all_months)
status_code_all_months=response_all_months.code
if status_code_all_months == 200:
print('status code = 200: The request succeeded, and the resource is returned.')
elif status_code_all_months == 404:
print('status code = 404: The requested resource does not exist.')
elif status_code_all_months == 500:
print('status code 500 = An unexpected error happened on the server side.')
elif status_code_all_months == 301 or status_code_all_months == 302 or status_code_all_months == 303:
print('status code = 301/302/303: The resource has moved to another URL.')
else:
print('status code =', status_code_all_months)
html_all_months = response_all_months.read().decode()
#Split into lines and print each line
lines_all_months = html_all_months.strip().split("\n")
for l in lines_all_months:
print(l)
status code = 200: The request succeeded, and the resource is returned.
<!DOCTYPE html>
<html lang="en">
<head>
<!-- Note: This data is made only available for educational purposes for use COMP41680 Assignment 2 -->
<title>Online News Archive</title>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1">
<meta name="robots" content="noindex">
<meta name="keywords" content="news,articles,news"/>
<link rel="shortcut icon" href="http://www.insight-centre.org/sites/all/themes/bootstrap_insight/favicon.ico" type="image/vnd.microsoft.icon" />
<meta property="og:title" content="Breaking News | International Headlines">
<meta property="og:site_name" content="News Archive">
<meta property="og:description" content="Latest news and more from the definitive brand of quality news.">
<link rel="stylesheet" href="css/bootstrap.min.css">
<script src="js/bootstrap.min.js"></script>
<style>
.main{ padding: 0; text-align: center;}
.footer{ padding: 6px;text-align: center; margin-top: 1em; }
h1
{
font-size: 180%;
margin-top: 15px;
margin-bottom: 15px;
}
ul {list-style-type: none;}
li { margin-top: 5px; }
</style>
</head>
<body>
<div class="container" style="margin-top: 2em;">
<div class="main">
<img src="images/banner.jpg" width="500" alt="banner"/>
<h1>News Article Archive</h1>
<p>Archive of all news headlines and stories, organised per month.</p>
<ul>
<li>Articles — <a href='month-jan-2017.html'>January</a> [118]</li>
<li>Articles — <a href='month-feb-2017.html'>February</a> [124]</li>
<li>Articles — <a href='month-mar-2017.html'>March</a> [116]</li>
<li>Articles — <a href='month-apr-2017.html'>April</a> [118]</li>
<li>Articles — <a href='month-may-2017.html'>May</a> [115]</li>
<li>Articles — <a href='month-jun-2017.html'>June</a> [115]</li>
<li>Articles — <a href='month-jul-2017.html'>July</a> [122]</li>
<li>Articles — <a href='month-aug-2017.html'>August</a> [116]</li>
<li>Articles — <a href='month-sep-2017.html'>September</a> [113]</li>
<li>Articles — <a href='month-oct-2017.html'>October</a> [124]</li>
<li>Articles — <a href='month-nov-2017.html'>November</a> [122]</li>
<li>Articles — <a href='month-dec-2017.html'>December</a> [115]</li>
</ul>
</div>
<div class="footer">
<span><a href="">Terms & Conditions</a> | <a href="">Privacy Policy</a> | <a href="">Cookie Information</a> </span><br/>
<span>© <span class="thisyear">2017</span> — Original rights holders</span>
</div>
</div>
</body>
</html>
We want to get links name endings for all 12 months (12 web pages: News Archive - Articles for given Month). We will extract that information from < a> tag
In [3]:
parser_all_months = bs4.BeautifulSoup(html_all_months,"html.parser")
all_text_all_months=[]
for match in parser_all_months.find_all("a"):
text_all_months = match.get_text()
#print(text_all_months)
all_text_all_months.append(text_all_months)
#print(all_text_all_months)
article_all_months=" ".join(all_text_all_months)
#Putting all the link name endings for web pages containing Articles for different months to a list link_names_months.
link_names_months=[]
for link in parser_all_months.findAll('a'):
link_names_months.append(link.get('href'))
#print(link_names_jan[0:])
link_names_months=link_names_months[0:-3] #removing '', '', ''
print(link_names_months)
['month-jan-2017.html', 'month-feb-2017.html', 'month-mar-2017.html', 'month-apr-2017.html', 'month-may-2017.html', 'month-jun-2017.html', 'month-jul-2017.html', 'month-aug-2017.html', 'month-sep-2017.html', 'month-oct-2017.html', 'month-nov-2017.html', 'month-dec-2017.html']
We want to retrieve all web pages corresponding to article URLs. We will use BeautifulSoup to extract the main body text containing the content of each news article.
But before doing that, we are going to extract the link name endings for web pages containing all Articles to a list link_names_articles_new.
Also, in this step I am saving the category labels for all articles to a separate list labels[].
In [4]:
link_names_articles=[]
articles_all_months=[]
labels=[]
labels_old=[]
for i in range(len(link_names_months)):
link_month='http://mlg.ucd.ie/modules/COMP41680/archive/'+link_names_months[i]
#Fetch the HTML code from the web page
response_month = urllib.request.urlopen(link_month)
status_code_month=response_month.code
if status_code_month == 200:
print('status code = 200: The request succeeded, and the resource is returned.')
elif status_code_month == 404:
print('status code = 404: The requested resource does not exist.')
elif status_code_month == 500:
print('status code 500 = An unexpected error happened on the server side.')
elif status_code_month == 301 or status_code_month == 302 or status_code_month == 303:
print('status code = 301/302/303: The resource has moved to another URL.')
else:
print('status code =', status_code_month)
html_month = response_month.read().decode()
#Split into lines and print each line
#lines_month = html_month.strip().split("\n")
#for l in lines_month:
# print(l)
parser_month = bs4.BeautifulSoup(html_month,"html.parser")
all_text_month=[]
for match in parser_month.find_all("body"):
text_month = match.get_text()
#print(text_month)
all_text_month.append(text_month)
article_month=" ".join(all_text_month)
#print(article_month) #not a list
#getting all the names for articles in a list
for link in parser_month.findAll('a'):
link_names_articles.append(link.get('href'))
#print(link_names_all_months[0:])
link_names_articles=link_names_articles[0:-4] #removing 'index.html', '', '', ''
link_names_articles_new = [x[:-5] for x in link_names_articles] # removing .html from names
#Category Labels
#Saving the category labels for all articles to a separate list labels[].
all_text_labels=[]
for match in parser_month.find_all("td"):
text_labels = match.get_text()
#print(text_labels)
all_text_labels.append(text_labels)
for i in range(2,len(all_text_labels),2):
labels_old.append(all_text_labels[i])
labels = [x[1:] for x in labels_old] #removing first unneccesery part from label names
#print(labels)
print('Number of category labels contining N/A labels',len(labels))
ax = sns.countplot(labels) #seaborn.countplot - Show value counts for a single categorical variable
ax.set_title("Distribution of the Labels (with N/A)")
#Lets remove four N/A labels with no articles from labels list
labels = [l for l in labels if l != 'N/A']
print('Number of category labels without N/A labels',len(labels))
#print(labels)
status code = 200: The request succeeded, and the resource is returned. Number of category labels contining N/A labels 122 Number of category labels without N/A labels 118 status code = 200: The request succeeded, and the resource is returned. Number of category labels contining N/A labels 249 Number of category labels without N/A labels 240 status code = 200: The request succeeded, and the resource is returned. Number of category labels contining N/A labels 371 Number of category labels without N/A labels 356 status code = 200: The request succeeded, and the resource is returned. Number of category labels contining N/A labels 493 Number of category labels without N/A labels 473 status code = 200: The request succeeded, and the resource is returned. Number of category labels contining N/A labels 610 Number of category labels without N/A labels 587 status code = 200: The request succeeded, and the resource is returned. Number of category labels contining N/A labels 728 Number of category labels without N/A labels 701 status code = 200: The request succeeded, and the resource is returned. Number of category labels contining N/A labels 855 Number of category labels without N/A labels 823 status code = 200: The request succeeded, and the resource is returned. Number of category labels contining N/A labels 974 Number of category labels without N/A labels 939 status code = 200: The request succeeded, and the resource is returned. Number of category labels contining N/A labels 1090 Number of category labels without N/A labels 1051 status code = 200: The request succeeded, and the resource is returned. Number of category labels contining N/A labels 1218 Number of category labels without N/A labels 1173 status code = 200: The request succeeded, and the resource is returned. Number of category labels contining N/A labels 1343 Number of category labels without N/A labels 1294 status code = 200: The request succeeded, and the resource is returned. Number of category labels contining N/A labels 1461 Number of category labels without N/A labels 1408
In [5]:
#print(labels)
#print(link_names_all_months_new)
print('There are',len(link_names_articles_new), 'web pages from which we need to extract the main body text containing the content of each news article, and', len(labels),'category labels')
There are 1408 web pages from which we need to extract the main body text containing the content of each news article, and 1408 category labels
From the plot we can see that labels do not have balanced distribution, hence we should apply the random under-sampling later on. Oversampling and undersampling in data analysis are techniques used to adjust the class distribution of a data set (i.e. the ratio between the different classes/categories represented).
In [6]:
#Plot a bar plot of the labels
ax = sns.countplot(labels) #seaborn.countplot - Show value counts for a single categorical variable
ax.set_title("Distribution of the Labels (without N/A)")
plt.show()
We need to extract the main body text containing the content of each news article. We can use BeautifulSoup to find all these tags and get the text between them. We can use < body> tags or combination of < p> and < b> tags. Note: In some articles Title is in < b> tab, while in some in < p>
In [7]:
#Extracting the main body text from the web pages containing the content of each news article.
articles_body=[]
aricles_b_p=[]
aricles_b_p_no_end=[]
link_article = []
for i in range(len(link_names_articles_new)):
link="http://mlg.ucd.ie/modules/COMP41680/archive/"+link_names_articles_new[i] +".html"
print('\n',link_names_articles_new[i])
response = urllib.request.urlopen(link)
status_code=response.code
if status_code == 200:
print('status code = 200: The request succeeded, and the resource is returned.')
elif status_code == 404:
print('status code = 404: The requested resource does not exist.')
elif status_code == 500:
print('status code 500 = An unexpected error happened on the server side.')
elif status_code == 301 or status_code == 302 or status_code == 303:
print('status code = 301/302/303: The resource has moved to another URL.')
else:
print('status code =', status_code)
html = response.read().decode()
#Split into lines and print each line
#lines = html.strip().split("\n")
#for l in lines:
# print(l)
parser = bs4.BeautifulSoup(html,"html.parser")
all_text_body=[]
#Extracting text between < body> tags
for match in parser.find_all("body"):
text = match.get_text()
#print(text)
all_text_body.append(text)
#print(all_text)
article_body=" ".join(all_text_body)
#print(article)
articles_body.append(article_body)
all_text_b_p=[]
#Extracting text between < b> and < p> tags
for match in parser.find_all("b"):
text = match.get_text()
#print(text)
all_text_b_p.append(text)
for match in parser.find_all("p"):
text = match.get_text()
#print(text)
all_text_b_p.append(text)
#print(all_text)
article_b_p=" ".join(all_text_b_p)
#print(article)
aricles_b_p.append(article_b_p)
#Removing: 'Return to article search results' or 'Comments are closed for this article.'
#These sentences at the end don't have much to do with the article category so we want to exclude them so
#they don't influence similarity between documents/articles
all_text_b_p_no_end = all_text_b_p[:-1]
article_b_p_no_end=" ".join(all_text_b_p_no_end)
#print(article)
aricles_b_p_no_end.append(article_b_p_no_end)
article-jan-0418 status code = 200: The request succeeded, and the resource is returned. article-jan-0027 status code = 200: The request succeeded, and the resource is returned. article-jan-0631 status code = 200: The request succeeded, and the resource is returned. article-jan-2105 status code = 200: The request succeeded, and the resource is returned. article-jan-3300 status code = 200: The request succeeded, and the resource is returned. article-jan-4187 status code = 200: The request succeeded, and the resource is returned. article-jan-1974 status code = 200: The request succeeded, and the resource is returned. article-jan-3666 status code = 200: The request succeeded, and the resource is returned. article-jan-2629 status code = 200: The request succeeded, and the resource is returned. article-jan-2415 status code = 200: The request succeeded, and the resource is returned. article-jan-4210 status code = 200: The request succeeded, and the resource is returned. article-jan-4789 status code = 200: The request succeeded, and the resource is returned. article-jan-3452 status code = 200: The request succeeded, and the resource is returned. article-jan-2428 status code = 200: The request succeeded, and the resource is returned. article-jan-4766 status code = 200: The request succeeded, and the resource is returned. article-jan-2595 status code = 200: The request succeeded, and the resource is returned. article-jan-2935 status code = 200: The request succeeded, and the resource is returned. article-jan-0578 status code = 200: The request succeeded, and the resource is returned. article-jan-3023 status code = 200: The request succeeded, and the resource is returned. article-jan-2356 status code = 200: The request succeeded, and the resource is returned. article-jan-1023 status code = 200: The request succeeded, and the resource is returned. article-jan-0641 status code = 200: The request succeeded, and the resource is returned. article-jan-2461 status code = 200: 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article-jan-2551 status code = 200: The request succeeded, and the resource is returned. article-jan-0989 status code = 200: The request succeeded, and the resource is returned. article-jan-0633 status code = 200: The request succeeded, and the resource is returned. article-jan-1566 status code = 200: The request succeeded, and the resource is returned. article-jan-0791 status code = 200: The request succeeded, and the resource is returned. article-jan-2691 status code = 200: The request succeeded, and the resource is returned. article-jan-3650 status code = 200: The request succeeded, and the resource is returned. article-jan-1867 status code = 200: The request succeeded, and the resource is returned. article-jan-2835 status code = 200: The request succeeded, and the resource is returned. article-jan-1079 status code = 200: The request succeeded, and the resource is returned. article-jan-4592 status code = 200: The request succeeded, and the resource is returned. article-jan-4111 status code = 200: The request succeeded, and the resource is returned. article-jan-0599 status code = 200: The request succeeded, and the resource is returned. article-jan-0969 status code = 200: The request succeeded, and the resource is returned. article-jan-2042 status code = 200: The request succeeded, and the resource is returned. article-jan-0050 status code = 200: The request succeeded, and the resource is returned. article-jan-4860 status code = 200: The request succeeded, and the resource is returned. article-jan-0180 status code = 200: The request succeeded, and the resource is returned. article-jan-1582 status code = 200: The request succeeded, and the resource is returned. article-jan-4456 status code = 200: The request succeeded, and the resource is returned. article-jan-3002 status code = 200: The request succeeded, and the resource is returned. article-jan-2380 status code = 200: The request succeeded, and the resource is returned. article-jan-3243 status code = 200: The request succeeded, and the resource is returned. article-jan-3151 status code = 200: The request succeeded, and the resource is returned. article-jan-2619 status code = 200: The request succeeded, and the resource is returned. article-jan-1811 status code = 200: The request succeeded, and the resource is returned. article-jan-3831 status code = 200: The request succeeded, and the resource is returned. article-jan-4567 status code = 200: The request succeeded, and the resource is returned. article-jan-3938 status code = 200: The request succeeded, and the resource is returned. article-jan-4175 status code = 200: The request succeeded, and the resource is returned. article-jan-0237 status code = 200: The request succeeded, and the resource is returned. article-jan-0924 status code = 200: The request succeeded, and the resource is returned. article-jan-1731 status code = 200: The request succeeded, and the resource is returned. article-jan-3981 status code = 200: The request succeeded, and the resource is returned. article-jan-2769 status code = 200: The request succeeded, and the resource is returned. article-jan-2140 status code = 200: The request succeeded, and the resource is returned. article-jan-3329 status code = 200: The request succeeded, and the resource is returned. article-jan-1087 status code = 200: The request succeeded, and the resource is returned. article-jan-0480 status code = 200: The request succeeded, and the resource is returned. article-jan-2591 status code = 200: The request succeeded, and the resource is returned. article-jan-2407 status code = 200: The request succeeded, and the resource is returned. article-jan-1520 status code = 200: The request succeeded, and the resource is returned. article-jan-3036 status code = 200: The request succeeded, and the resource is returned. article-jan-1051 status code = 200: The request succeeded, and the resource is returned. article-jan-2864 status code = 200: The request succeeded, and the 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article-jan-0587 status code = 200: The request succeeded, and the resource is returned. article-jan-1650 status code = 200: The request succeeded, and the resource is returned. article-jan-0867 status code = 200: The request succeeded, and the resource is returned. article-jan-0457 status code = 200: The request succeeded, and the resource is returned. article-jan-2075 status code = 200: The request succeeded, and the resource is returned. article-jan-3244 status code = 200: The request succeeded, and the resource is returned. article-feb-3392 status code = 200: The request succeeded, and the resource is returned. article-feb-0704 status code = 200: The request succeeded, and the resource is returned. article-feb-0913 status code = 200: The request succeeded, and the resource is returned. article-feb-3975 status code = 200: The request succeeded, and the resource is returned. article-feb-0641 status code = 200: The request succeeded, and the resource is returned. article-feb-0608 status code = 200: The request succeeded, and the resource is returned. article-feb-4436 status code = 200: The request succeeded, and the resource is returned. article-feb-0930 status code = 200: The request succeeded, and the resource is returned. article-feb-4492 status code = 200: The request succeeded, and the resource is returned. article-feb-4474 status code = 200: The request succeeded, and the resource is returned. article-feb-4340 status code = 200: The request succeeded, and the resource is returned. article-feb-1116 status code = 200: The request succeeded, and the resource is returned. article-feb-2873 status code = 200: The request succeeded, and the resource is returned. article-feb-2303 status code = 200: The request succeeded, and the resource is returned. article-feb-2977 status code = 200: The request succeeded, and the resource is returned. article-feb-4547 status code = 200: The request succeeded, and the resource is returned. article-feb-3154 status code = 200: The request succeeded, and the resource is returned. article-feb-1466 status code = 200: The request succeeded, and the resource is returned. article-feb-3116 status code = 200: The request succeeded, and the resource is returned. article-feb-3641 status code = 200: The request succeeded, and the resource is returned. article-feb-3724 status code = 200: The request succeeded, and the resource is returned. article-feb-1553 status code = 200: The request succeeded, and the resource is returned. article-feb-0192 status code = 200: The request succeeded, and the resource is returned. article-feb-2830 status code = 200: The request succeeded, and the resource is returned. article-feb-2366 status code = 200: The request succeeded, and the resource is returned. article-feb-3261 status code = 200: The request succeeded, and the resource is returned. article-feb-2526 status code = 200: The request succeeded, and the resource is returned. article-feb-3097 status code = 200: The request succeeded, and the resource is returned. article-feb-1454 status code = 200: The request succeeded, and the resource is returned. article-feb-0258 status code = 200: The request succeeded, and the resource is returned. article-feb-4548 status code = 200: The request succeeded, and the resource is returned. article-feb-0679 status code = 200: The request succeeded, and the resource is returned. article-feb-1000 status code = 200: The request succeeded, and the resource is returned. article-feb-1821 status code = 200: The request succeeded, and the resource is returned. article-feb-4494 status code = 200: The request succeeded, and the resource is returned. article-feb-0250 status code = 200: The request succeeded, and the resource is returned. article-feb-2382 status code = 200: The request succeeded, and the resource is returned. article-feb-4407 status code = 200: The request succeeded, and the resource is returned. article-feb-3768 status code = 200: The request succeeded, and the resource is returned. article-feb-3375 status code = 200: The request succeeded, and the resource is returned. article-feb-2553 status code = 200: The request succeeded, and the resource is returned. article-feb-2750 status code = 200: The request succeeded, and the resource is returned. article-feb-0829 status code = 200: The request succeeded, and the resource is returned. article-feb-3941 status code = 200: The request succeeded, and the resource is returned. article-feb-1333 status code = 200: The request succeeded, and the resource is returned. article-feb-3708 status code = 200: The request succeeded, and the resource is returned. article-feb-1684 status code = 200: The request succeeded, and the resource is returned. article-feb-4876 status code = 200: The request succeeded, and the resource is returned. article-feb-1493 status code = 200: The request succeeded, and the resource is returned. article-feb-0113 status code = 200: The request succeeded, and the resource is returned. article-feb-2502 status code = 200: The request succeeded, and the resource is returned. article-feb-2351 status code = 200: The request succeeded, and the resource is returned. article-feb-1245 status code = 200: The request succeeded, and the resource is returned. article-feb-2809 status code = 200: The request succeeded, and the resource is returned. article-feb-4569 status code = 200: The request succeeded, and the resource is returned. article-feb-4323 status code = 200: The request succeeded, and the resource is returned. article-feb-3937 status code = 200: The request succeeded, and the resource is returned. article-feb-1582 status code = 200: The request succeeded, and the resource is returned. article-feb-1312 status code = 200: The request succeeded, and the resource is returned. article-feb-2836 status code = 200: The request succeeded, and the resource is returned. article-feb-0505 status code = 200: The request succeeded, and the resource is returned. article-feb-3556 status code = 200: The request succeeded, and the resource is returned. article-feb-4691 status code = 200: The request succeeded, and the resource is returned. article-feb-4543 status code = 200: The request succeeded, and the resource is returned. article-feb-3028 status code = 200: The request succeeded, and the resource is returned. article-feb-1960 status code = 200: The request succeeded, and the resource is returned. article-feb-4127 status code = 200: The request succeeded, and the resource is returned. article-feb-4247 status code = 200: The request succeeded, and the resource is returned. article-feb-2114 status code = 200: The request succeeded, and the resource is returned. article-feb-0571 status code = 200: The request succeeded, and the resource is returned. article-feb-0451 status code = 200: The request succeeded, and the resource is returned. article-feb-1201 status code = 200: The request succeeded, and the resource is returned. article-feb-4578 status code = 200: The request succeeded, and the resource is returned. article-feb-3474 status code = 200: The request succeeded, and the resource is returned. article-feb-4987 status code = 200: The request succeeded, and the resource is returned. article-feb-4718 status code = 200: The request succeeded, and the resource is returned. article-feb-2135 status code = 200: The request succeeded, and the resource is returned. article-feb-1118 status code = 200: The request succeeded, and the resource is returned. article-feb-4768 status code = 200: The request succeeded, and the resource is returned. article-feb-4017 status code = 200: The request succeeded, and the resource is returned. article-feb-1005 status code = 200: The request succeeded, and the resource is returned. article-feb-0504 status code = 200: The request succeeded, and the resource is returned. article-feb-1308 status code = 200: The request succeeded, and the resource is returned. article-feb-2371 status code = 200: The request succeeded, and the resource is returned. article-feb-3067 status code = 200: The request succeeded, and the resource is returned. article-feb-1134 status code = 200: The request succeeded, and the resource is returned. article-feb-1004 status code = 200: The request succeeded, and the resource is returned. article-feb-0814 status code = 200: The request succeeded, and the resource is returned. article-feb-4229 status code = 200: The request succeeded, and the resource is returned. article-feb-0630 status code = 200: The request succeeded, and the resource is returned. article-feb-2030 status code = 200: The request succeeded, and the resource is returned. article-feb-1336 status code = 200: The request succeeded, and the resource is returned. article-feb-3074 status code = 200: The request succeeded, and the resource is returned. article-feb-1812 status code = 200: The request succeeded, and the resource is returned. article-feb-1149 status code = 200: The request succeeded, and the resource is returned. article-feb-0800 status code = 200: The request succeeded, and the resource is returned. article-feb-2206 status code = 200: The request succeeded, and the resource is returned. article-feb-0110 status code = 200: The request succeeded, and the resource is returned. article-feb-3700 status code = 200: The request succeeded, and the resource is returned. article-feb-4539 status code = 200: The request succeeded, and the resource is returned. article-feb-0707 status code = 200: The request succeeded, and the resource is returned. article-feb-2055 status code = 200: The request succeeded, and the resource is returned. article-feb-4838 status code = 200: The request succeeded, and the resource is returned. article-feb-2802 status code = 200: The request succeeded, and the resource is returned. article-feb-2640 status code = 200: The request succeeded, and the resource is returned. article-feb-4399 status code = 200: The request succeeded, and the resource is returned. article-feb-0406 status code = 200: The request succeeded, and the resource is returned. article-feb-2304 status code = 200: The request succeeded, and the resource is returned. article-feb-2170 status code = 200: The request succeeded, and the resource is returned. article-feb-3783 status code = 200: The request succeeded, and the resource is returned. article-feb-4402 status code = 200: The request succeeded, and the resource is returned. article-feb-1028 status code = 200: The request succeeded, and the resource is returned. article-feb-2316 status code = 200: The request succeeded, and the resource is returned. article-feb-0243 status code = 200: The request succeeded, and the resource is returned. article-feb-2017 status code = 200: The request succeeded, and the resource is returned. article-feb-2660 status code = 200: The request succeeded, and the resource is returned. article-feb-0224 status code = 200: The request succeeded, and the resource is returned. article-feb-2955 status code = 200: The request succeeded, and the resource is returned. article-feb-4616 status code = 200: The request succeeded, and the resource is returned. article-feb-0576 status code = 200: The request succeeded, and the resource is returned. article-feb-0519 status code = 200: The request succeeded, and the resource is returned. article-feb-1952 status code = 200: The request succeeded, and the resource is returned. article-mar-1126 status code = 200: The request succeeded, and the resource is returned. article-mar-3331 status code = 200: The request succeeded, and the resource is returned. article-mar-3141 status code = 200: The request succeeded, and the resource is returned. article-mar-3697 status code = 200: The request succeeded, and the resource is returned. article-mar-3655 status code = 200: The request succeeded, and the resource is returned. article-mar-2120 status code = 200: The request succeeded, and the resource is returned. article-mar-2380 status code = 200: The request succeeded, and the resource is returned. article-mar-1445 status code = 200: The request succeeded, and the resource is returned. article-mar-1627 status code = 200: The request succeeded, and the resource is returned. article-mar-0220 status code = 200: The request succeeded, and the resource is returned. article-mar-1595 status code = 200: The request succeeded, and the resource is returned. article-mar-4085 status code = 200: The request succeeded, and the resource is returned. article-mar-2495 status code = 200: The request succeeded, and the resource is returned. article-mar-4902 status code = 200: The request succeeded, and the resource is returned. article-mar-3484 status code = 200: The request succeeded, and the resource is returned. article-mar-1905 status code = 200: The request succeeded, and the resource is returned. article-mar-1702 status code = 200: The request succeeded, and the resource is returned. article-mar-4607 status code = 200: The request succeeded, and the resource is returned. article-mar-4510 status code = 200: The request succeeded, and the resource is returned. article-mar-1543 status code = 200: The request succeeded, and the resource is returned. article-mar-0210 status code = 200: The request succeeded, and the resource is returned. article-mar-1989 status code = 200: The request succeeded, and the resource is returned. article-mar-4293 status code = 200: The request succeeded, and the resource is returned. article-mar-0521 status code = 200: The request succeeded, and the resource is returned. article-mar-3692 status code = 200: The request succeeded, and the resource is returned. article-mar-2874 status code = 200: The request succeeded, and the resource is returned. article-mar-3420 status code = 200: The request succeeded, and the resource is returned. article-mar-3360 status code = 200: The request succeeded, and the resource is returned. article-mar-0037 status code = 200: The request succeeded, and the resource is returned. article-mar-2432 status code = 200: The request succeeded, and the resource is returned. article-mar-1244 status code = 200: The request succeeded, and the resource is returned. article-mar-1251 status code = 200: The request succeeded, and the resource is returned. article-mar-0183 status code = 200: The request succeeded, and the resource is returned. article-mar-2000 status code = 200: The request succeeded, and the resource is returned. article-mar-3083 status code = 200: The request succeeded, and the resource is returned. article-mar-3722 status code = 200: The request succeeded, and the resource is returned. article-mar-1165 status code = 200: The request succeeded, and the resource is returned. article-mar-2123 status code = 200: The request succeeded, and the resource is returned. article-mar-3909 status code = 200: The request succeeded, and the resource is returned. article-mar-0579 status code = 200: The request succeeded, and the resource is returned. article-mar-3023 status code = 200: The request succeeded, and the resource is returned. article-mar-0977 status code = 200: The request succeeded, and the resource is returned. article-mar-4216 status code = 200: The request succeeded, and the resource is returned. article-mar-2195 status code = 200: The request succeeded, and the resource is returned. article-mar-4576 status code = 200: The request succeeded, and the resource is returned. article-mar-2382 status code = 200: The request succeeded, and the resource is returned. article-mar-0012 status code = 200: The request succeeded, and the resource is returned. article-mar-4343 status code = 200: The request succeeded, and the resource is returned. article-mar-3567 status code = 200: The request succeeded, and the resource is returned. article-mar-1674 status code = 200: The request succeeded, and the resource is returned. article-mar-3810 status code = 200: The request succeeded, and the resource is returned. article-mar-3301 status code = 200: The request succeeded, and the resource is returned. article-mar-2795 status code = 200: The request succeeded, and the resource is returned. article-mar-3037 status code = 200: The request succeeded, and the resource is returned. article-mar-1629 status code = 200: The request succeeded, and the resource is returned. article-mar-0481 status code = 200: The request succeeded, and the resource is returned. article-mar-0388 status code = 200: The request succeeded, and the resource is returned. article-mar-1453 status code = 200: The request succeeded, and the resource is returned. article-mar-1606 status code = 200: The request succeeded, and the resource is returned. article-mar-1230 status code = 200: The request succeeded, and the resource is returned. article-mar-3153 status code = 200: The request succeeded, and the resource is returned. article-mar-0170 status code = 200: The request succeeded, and the resource is returned. article-mar-4481 status code = 200: The request succeeded, and the resource is returned. article-mar-4754 status code = 200: The request succeeded, and the resource is returned. article-mar-3612 status code = 200: The request succeeded, and the resource is returned. article-mar-3927 status code = 200: The request succeeded, and the resource is returned. article-mar-4340 status code = 200: The request succeeded, and the resource is returned. article-mar-1335 status code = 200: The request succeeded, and the resource is returned. article-mar-2040 status code = 200: The request succeeded, and the resource is returned. article-mar-0033 status code = 200: The request succeeded, and the resource is returned. article-mar-2536 status code = 200: The request succeeded, and the resource is returned. article-mar-4512 status code = 200: The request succeeded, and the resource is returned. article-mar-4615 status code = 200: The request succeeded, and the resource is returned. article-mar-4246 status code = 200: The request succeeded, and the resource is returned. article-mar-4861 status code = 200: The request succeeded, and the resource is returned. article-mar-4279 status code = 200: The request succeeded, and the resource is returned. article-mar-4599 status code = 200: The request succeeded, and the resource is returned. article-mar-2276 status code = 200: The request succeeded, and the resource is returned. article-mar-1739 status code = 200: The request succeeded, and the resource is returned. article-mar-4407 status code = 200: The request succeeded, and the resource is returned. article-mar-3144 status code = 200: The request succeeded, and the resource is returned. article-mar-0182 status code = 200: The request succeeded, and the resource is returned. article-mar-2488 status code = 200: The request succeeded, and the resource is returned. article-mar-4584 status code = 200: The request succeeded, and the resource is returned. article-mar-4102 status code = 200: The request succeeded, and the resource is returned. article-mar-3792 status code = 200: The request succeeded, and the resource is returned. article-mar-0131 status code = 200: The request succeeded, and the resource is returned. article-mar-4398 status code = 200: The request succeeded, and the resource is returned. article-mar-0463 status code = 200: The request succeeded, and the resource is returned. article-mar-0029 status code = 200: The request succeeded, and the resource is returned. article-mar-4749 status code = 200: The request succeeded, and the resource is returned. article-mar-3392 status code = 200: The request succeeded, and the resource is returned. article-mar-0952 status code = 200: The request succeeded, and the resource is returned. article-mar-1203 status code = 200: The request succeeded, and the resource is returned. article-mar-2048 status code = 200: The request succeeded, and the resource is returned. article-mar-3318 status code = 200: The request succeeded, and the resource is returned. article-mar-2788 status code = 200: The request succeeded, and the resource is returned. article-mar-4562 status code = 200: The request succeeded, and the resource is returned. article-mar-0620 status code = 200: The request succeeded, and the resource is returned. article-mar-4001 status code = 200: The request succeeded, and the resource is returned. article-mar-1599 status code = 200: The request succeeded, and the resource is returned. article-mar-2785 status code = 200: The request succeeded, and the resource is returned. article-mar-1454 status code = 200: The request succeeded, and the resource is returned. article-mar-1913 status code = 200: The request succeeded, and the resource is returned. article-mar-4140 status code = 200: The request succeeded, and the resource is returned. article-mar-4357 status code = 200: The request succeeded, and the resource is returned. article-mar-4898 status code = 200: The request succeeded, and the resource is returned. article-mar-0550 status code = 200: The request succeeded, and the resource is returned. article-mar-2508 status code = 200: The request succeeded, and the resource is returned. article-mar-2039 status code = 200: The request succeeded, and the resource is returned. article-mar-4111 status code = 200: The request succeeded, and the resource is returned. article-mar-4582 status code = 200: The request succeeded, and the resource is returned. article-mar-3651 status code = 200: The request succeeded, and the resource is returned. article-mar-3663 status code = 200: The request succeeded, and the resource is returned. article-mar-4500 status code = 200: The request succeeded, and the resource is returned. article-mar-1916 status code = 200: The request succeeded, and the resource is returned. article-apr-1897 status code = 200: The request succeeded, and the resource is returned. article-apr-2967 status code = 200: The request succeeded, and the resource is returned. article-apr-2939 status code = 200: The request succeeded, and the resource is returned. article-apr-0145 status code = 200: The request succeeded, and the resource is returned. article-apr-1938 status code = 200: The request succeeded, and the resource is returned. article-apr-1748 status code = 200: The request succeeded, and the resource is returned. article-apr-2559 status code = 200: The request succeeded, and the resource is returned. article-apr-1306 status code = 200: The request succeeded, and the resource is returned. article-apr-2454 status code = 200: The request succeeded, and the resource is returned. article-apr-0039 status code = 200: The request succeeded, and the resource is returned. article-apr-1044 status code = 200: The request succeeded, and the resource is returned. article-apr-2151 status code = 200: The request succeeded, and the resource is returned. article-apr-2808 status code = 200: The request succeeded, and the resource is returned. article-apr-4324 status code = 200: The request succeeded, and the resource is returned. article-apr-2873 status code = 200: The request succeeded, and the resource is returned. article-apr-4741 status code = 200: The request succeeded, and the resource is returned. article-apr-0433 status code = 200: The request succeeded, and the resource is returned. article-apr-4890 status code = 200: The request succeeded, and the resource is returned. article-apr-4575 status code = 200: The request succeeded, and the resource is returned. article-apr-0228 status code = 200: The request succeeded, and the resource is returned. article-apr-4827 status code = 200: The request succeeded, and the resource is returned. article-apr-1926 status code = 200: The request succeeded, and the resource is returned. article-apr-1571 status code = 200: The request succeeded, and the resource is returned. article-apr-1904 status code = 200: The request succeeded, and the resource is returned. article-apr-3933 status code = 200: The request succeeded, and the resource is returned. article-apr-4960 status code = 200: The request succeeded, and the resource is returned. article-apr-4241 status code = 200: The request succeeded, and the resource is returned. article-apr-4901 status code = 200: The request succeeded, and the resource is returned. article-apr-0724 status code = 200: The request succeeded, and the resource is returned. article-apr-4907 status code = 200: The request succeeded, and the resource is returned. article-apr-3088 status code = 200: The request succeeded, and the resource is returned. article-apr-0395 status code = 200: The request succeeded, and the resource is returned. article-apr-4460 status code = 200: The request succeeded, and the resource is returned. article-apr-0524 status code = 200: The request succeeded, and the resource is returned. article-apr-3536 status code = 200: The request succeeded, and the resource is returned. article-apr-4694 status code = 200: The request succeeded, and the resource is returned. article-apr-3827 status code = 200: The request succeeded, and the resource is returned. article-apr-2270 status code = 200: The request succeeded, and the resource is returned. article-apr-0205 status code = 200: The request succeeded, and the resource is returned. article-apr-1858 status code = 200: The request succeeded, and the resource is returned. article-apr-3390 status code = 200: The request succeeded, and the resource is returned. article-apr-3841 status code = 200: The request succeeded, and the resource is returned. article-apr-4467 status code = 200: The request succeeded, and the resource is returned. article-apr-3842 status code = 200: The request succeeded, and the resource is returned. article-apr-3718 status code = 200: The request succeeded, and the resource is returned. article-apr-0784 status code = 200: The request succeeded, and the resource is returned. article-apr-2677 status code = 200: The request succeeded, and the resource is returned. article-apr-3475 status code = 200: The request succeeded, and the resource is returned. article-apr-2558 status code = 200: The request succeeded, and the resource is returned. article-apr-2706 status code = 200: The request succeeded, and the resource is returned. article-apr-4921 status code = 200: The request succeeded, and the resource is returned. article-apr-0240 status code = 200: The request succeeded, and the resource is returned. article-apr-2106 status code = 200: The request succeeded, and the resource is returned. article-apr-0622 status code = 200: The request succeeded, and the resource is returned. article-apr-4699 status code = 200: The request succeeded, and the resource is returned. article-apr-4622 status code = 200: The request succeeded, and the resource is returned. article-apr-2061 status code = 200: The request succeeded, and the resource is returned. article-apr-0646 status code = 200: The request succeeded, and the resource is returned. article-apr-4064 status code = 200: The request succeeded, and the resource is returned. article-apr-4854 status code = 200: The request succeeded, and the resource is returned. article-apr-4446 status code = 200: The request succeeded, and the resource is returned. article-apr-0150 status code = 200: The request succeeded, and the resource is returned. article-apr-3146 status code = 200: The request succeeded, and the resource is returned. article-apr-1270 status code = 200: The request succeeded, and the resource is returned. article-apr-2879 status code = 200: The request succeeded, and the resource is returned. article-apr-1276 status code = 200: The request succeeded, and the resource is returned. article-apr-0061 status code = 200: The request succeeded, and the resource is returned. article-apr-1620 status code = 200: The request succeeded, and the resource is returned. article-apr-2704 status code = 200: The request succeeded, and the resource is returned. article-apr-4823 status code = 200: The request succeeded, and the resource is returned. article-apr-0564 status code = 200: The request succeeded, and the resource is returned. article-apr-0338 status code = 200: The request succeeded, and the resource is returned. article-apr-4399 status code = 200: The request succeeded, and the resource is returned. article-apr-4686 status code = 200: The request succeeded, and the resource is returned. article-apr-2813 status code = 200: The request succeeded, and the resource is returned. article-apr-0953 status code = 200: The request succeeded, and the resource is returned. article-apr-2019 status code = 200: The request succeeded, and the resource is returned. article-apr-0042 status code = 200: The request succeeded, and the resource is returned. article-apr-4569 status code = 200: The request succeeded, and the resource is returned. article-apr-2560 status code = 200: The request succeeded, and the resource is returned. article-apr-1918 status code = 200: The request succeeded, and the resource is returned. article-apr-0857 status code = 200: The request succeeded, and the resource is returned. article-apr-0028 status code = 200: The request succeeded, and the resource is returned. article-apr-1068 status code = 200: The request succeeded, and the resource is returned. article-apr-4171 status code = 200: The request succeeded, and the resource is returned. article-apr-4618 status code = 200: The request succeeded, and the resource is returned. article-apr-1300 status code = 200: The request succeeded, and the resource is returned. article-apr-1815 status code = 200: The request succeeded, and the resource is returned. article-apr-3833 status code = 200: The request succeeded, and the resource is returned. article-apr-1205 status code = 200: The request succeeded, and the resource is returned. article-apr-3471 status code = 200: The request succeeded, and the resource is returned. article-apr-4435 status code = 200: The request succeeded, and the resource is returned. article-apr-0484 status code = 200: The request succeeded, and the resource is returned. article-apr-1009 status code = 200: The request succeeded, and the resource is returned. article-apr-0361 status code = 200: The request succeeded, and the resource is returned. article-apr-4474 status code = 200: The request succeeded, and the resource is returned. article-apr-3423 status code = 200: The request succeeded, and the resource is returned. article-apr-3100 status code = 200: The request succeeded, and the resource is returned. article-apr-0865 status code = 200: The request succeeded, and the resource is returned. article-apr-0134 status code = 200: The request succeeded, and the resource is returned. article-apr-2219 status code = 200: The request succeeded, and the resource is returned. article-apr-0182 status code = 200: The request succeeded, and the resource is returned. article-apr-4228 status code = 200: The request succeeded, and the resource is returned. article-apr-3753 status code = 200: The request succeeded, and the resource is returned. article-apr-2040 status code = 200: The request succeeded, and the resource is returned. article-apr-4036 status code = 200: The request succeeded, and the resource is returned. article-apr-4395 status code = 200: The request succeeded, and the resource is returned. article-apr-4277 status code = 200: The request succeeded, and the resource is returned. article-apr-1102 status code = 200: The request succeeded, and the resource is returned. article-apr-3992 status code = 200: The request succeeded, and the resource is returned. article-apr-3250 status code = 200: The request succeeded, and the resource is returned. article-apr-4031 status code = 200: The request succeeded, and the resource is returned. article-apr-0896 status code = 200: The request succeeded, and the resource is returned. article-apr-3567 status code = 200: The request succeeded, and the resource is returned. article-apr-3586 status code = 200: The request succeeded, and the resource is returned. article-apr-4951 status code = 200: The request succeeded, and the resource is returned. article-apr-0345 status code = 200: The request succeeded, and the resource is returned. article-may-0284 status code = 200: The request succeeded, and the resource is returned. article-may-3980 status code = 200: The request succeeded, and the resource is returned. article-may-2027 status code = 200: The request succeeded, and the resource is returned. article-may-2024 status code = 200: The request succeeded, and the resource is returned. article-may-2738 status code = 200: The request succeeded, and the resource is returned. article-may-4424 status code = 200: The request succeeded, and the resource is returned. article-may-2659 status code = 200: The request succeeded, and the resource is returned. article-may-4512 status code = 200: The request succeeded, and the resource is returned. article-may-0131 status code = 200: The request succeeded, and the resource is returned. article-may-2874 status code = 200: The request succeeded, and the resource is returned. article-may-2905 status code = 200: The request succeeded, and the resource is returned. article-may-1663 status code = 200: The request succeeded, and the resource is returned. article-may-1702 status code = 200: The request succeeded, and the resource is returned. article-may-4777 status code = 200: The request succeeded, and the resource is returned. article-may-2166 status code = 200: The request succeeded, and the resource is returned. article-may-1662 status code = 200: The request succeeded, and the resource is returned. article-may-1837 status code = 200: The request succeeded, and the resource is returned. article-may-1887 status code = 200: The request succeeded, and the resource is returned. article-may-3273 status code = 200: The request succeeded, and the resource is returned. article-may-2294 status code = 200: The request succeeded, and the resource is returned. article-may-3257 status code = 200: The request succeeded, and the resource is returned. article-may-2723 status code = 200: The request succeeded, and the resource is returned. article-may-1549 status code = 200: The request succeeded, and the resource is returned. article-may-0149 status code = 200: The request succeeded, and the resource is returned. article-may-2122 status code = 200: The request succeeded, and the resource is returned. article-may-4336 status code = 200: The request succeeded, and the resource is returned. article-may-0706 status code = 200: The request succeeded, and the resource is returned. article-may-0573 status code = 200: The request succeeded, and the resource is returned. article-may-2915 status code = 200: The request succeeded, and the resource is returned. article-may-1785 status code = 200: The request succeeded, and the resource is returned. article-may-1306 status code = 200: The request succeeded, and the resource is returned. article-may-2222 status code = 200: The request succeeded, and the resource is returned. article-may-1674 status code = 200: The request succeeded, and the resource is returned. article-may-3633 status code = 200: The request succeeded, and the resource is returned. article-may-3723 status code = 200: The request succeeded, and the resource is returned. article-may-2499 status code = 200: The request succeeded, and the resource is returned. article-may-2400 status code = 200: The request succeeded, and the resource is returned. article-may-2476 status code = 200: The request succeeded, and the resource is returned. article-may-2112 status code = 200: The request succeeded, and the resource is returned. article-may-2581 status code = 200: The request succeeded, and the resource is returned. article-may-2727 status code = 200: The request succeeded, and the resource is returned. article-may-0713 status code = 200: The request succeeded, and the resource is returned. article-may-4324 status code = 200: The request succeeded, and the resource is returned. article-may-4280 status code = 200: The request succeeded, and the resource is returned. article-may-0143 status code = 200: The request succeeded, and the resource is returned. article-may-2440 status code = 200: The request succeeded, and the resource is returned. article-may-1036 status code = 200: The request succeeded, and the resource is returned. article-may-0966 status code = 200: The request succeeded, and the resource is returned. article-may-4655 status code = 200: The request succeeded, and the resource is returned. article-may-2796 status code = 200: The request succeeded, and the resource is returned. article-may-0305 status code = 200: The request succeeded, and the resource is returned. article-may-2491 status code = 200: The request succeeded, and the resource is returned. article-may-2781 status code = 200: The request succeeded, and the resource is returned. article-may-1795 status code = 200: The request succeeded, and the resource is returned. article-may-0332 status code = 200: The request succeeded, and the resource is returned. article-may-4014 status code = 200: The request succeeded, and the resource is returned. article-may-4886 status code = 200: The request succeeded, and the resource is returned. article-may-4104 status code = 200: The request succeeded, and the resource is returned. article-may-1112 status code = 200: The request succeeded, and the resource is returned. article-may-0180 status code = 200: The request succeeded, and the resource is returned. article-may-1740 status code = 200: The request succeeded, and the resource is returned. article-may-4133 status code = 200: The request succeeded, and the resource is returned. article-may-0916 status code = 200: The request succeeded, and the resource is returned. article-may-3761 status code = 200: The request succeeded, and the resource is returned. article-may-4517 status code = 200: The request succeeded, and the resource is returned. article-may-1326 status code = 200: The request succeeded, and the resource is returned. article-may-0373 status code = 200: The request succeeded, and the resource is returned. article-may-4484 status code = 200: The request succeeded, and the resource is returned. article-may-4229 status code = 200: The request succeeded, and the resource is returned. article-may-4474 status code = 200: The request succeeded, and the resource is returned. article-may-1687 status code = 200: The request succeeded, and the resource is returned. article-may-1905 status code = 200: The request succeeded, and the resource is returned. article-may-2611 status code = 200: The request succeeded, and the resource is returned. article-may-4895 status code = 200: The request succeeded, and the resource is returned. article-may-0016 status code = 200: The request succeeded, and the resource is returned. article-may-1238 status code = 200: The request succeeded, and the resource is returned. article-may-1947 status code = 200: The request succeeded, and the resource is returned. article-may-2512 status code = 200: The request succeeded, and the resource is returned. article-may-1598 status code = 200: The request succeeded, and the resource is returned. article-may-4585 status code = 200: The request succeeded, and the resource is returned. article-may-4874 status code = 200: The request succeeded, and the resource is returned. article-may-0488 status code = 200: The request succeeded, and the resource is returned. article-may-0292 status code = 200: The request succeeded, and the resource is returned. article-may-3235 status code = 200: The request succeeded, and the resource is returned. article-may-1464 status code = 200: The request succeeded, and the resource is returned. article-may-2443 status code = 200: The request succeeded, and the resource is returned. article-may-2260 status code = 200: The request succeeded, and the resource is returned. article-may-3908 status code = 200: The request succeeded, and the resource is returned. article-may-2514 status code = 200: The request succeeded, and the resource is returned. article-may-0985 status code = 200: The request succeeded, and the resource is returned. article-may-3855 status code = 200: The request succeeded, and the resource is returned. article-may-0057 status code = 200: The request succeeded, and the resource is returned. article-may-0549 status code = 200: The request succeeded, and the resource is returned. article-may-0738 status code = 200: The request succeeded, and the resource is returned. article-may-1288 status code = 200: The request succeeded, and the resource is returned. article-may-2467 status code = 200: The request succeeded, and the resource is returned. article-may-2263 status code = 200: The request succeeded, and the resource is returned. article-may-0648 status code = 200: The request succeeded, and the resource is returned. article-may-3222 status code = 200: The request succeeded, and the resource is returned. article-may-1355 status code = 200: The request succeeded, and the resource is returned. article-may-3184 status code = 200: The request succeeded, and the resource is returned. article-may-2365 status code = 200: The request succeeded, and the resource is returned. article-may-0720 status code = 200: The request succeeded, and the resource is returned. article-may-3287 status code = 200: The request succeeded, and the resource is returned. article-may-2299 status code = 200: The request succeeded, and the resource is returned. article-may-0748 status code = 200: The request succeeded, and the resource is returned. article-may-4097 status code = 200: The request succeeded, and the resource is returned. article-may-2131 status code = 200: The request succeeded, and the resource is returned. article-may-3461 status code = 200: The request succeeded, and the resource is returned. article-may-4326 status code = 200: The request succeeded, and the resource is returned. article-may-2814 status code = 200: The request succeeded, and the resource is returned. article-may-2271 status code = 200: The request succeeded, and the resource is returned. article-may-3619 status code = 200: The request succeeded, and the resource is returned. article-may-0996 status code = 200: The request succeeded, and the resource is returned. article-jun-1972 status code = 200: The request succeeded, and the resource is returned. article-jun-1987 status code = 200: The request succeeded, and the resource is returned. article-jun-0716 status code = 200: The request succeeded, and the resource is returned. article-jun-2935 status code = 200: The request succeeded, and the resource is returned. article-jun-4840 status code = 200: The request succeeded, and the resource is returned. article-jun-4479 status code = 200: The request succeeded, and the resource is returned. article-jun-1568 status code = 200: The request succeeded, and the resource is returned. article-jun-1579 status code = 200: The request succeeded, and the resource is returned. article-jun-3219 status code = 200: The request succeeded, and the resource is returned. article-jun-1778 status code = 200: The request succeeded, and the resource is returned. article-jun-1644 status code = 200: The request succeeded, and the resource is returned. article-jun-2609 status code = 200: The request succeeded, and the resource is returned. article-jun-1346 status code = 200: The request succeeded, and the resource is returned. article-jun-3633 status code = 200: The request succeeded, and the resource is returned. article-jun-3298 status code = 200: The request succeeded, and the resource is returned. article-jun-3624 status code = 200: The request succeeded, and the resource is returned. article-jun-0532 status code = 200: The request succeeded, and the resource is returned. article-jun-2071 status code = 200: The request succeeded, and the resource is returned. article-jun-1491 status code = 200: The request succeeded, and the resource is returned. article-jun-1294 status code = 200: The request succeeded, and the resource is returned. article-jun-1801 status code = 200: The request succeeded, and the resource is returned. article-jun-4596 status code = 200: The request succeeded, and the resource is returned. article-jun-4483 status code = 200: The request succeeded, and the resource is returned. article-jun-0384 status code = 200: The request succeeded, and the resource is returned. article-jun-3025 status code = 200: The request succeeded, and the resource is returned. article-jun-3207 status code = 200: The request succeeded, and the resource is returned. article-jun-3422 status code = 200: The request succeeded, and the resource is returned. article-jun-3285 status code = 200: The request succeeded, and the resource is returned. article-jun-1131 status code = 200: The request succeeded, and the resource is returned. article-jun-4266 status code = 200: The request succeeded, and the resource is returned. article-jun-4362 status code = 200: The request succeeded, and the resource is returned. article-jun-0537 status code = 200: The request succeeded, and the resource is returned. article-jun-1448 status code = 200: The request succeeded, and the resource is returned. article-jun-4007 status code = 200: The request succeeded, and the resource is returned. article-jun-3887 status code = 200: The request succeeded, and the resource is returned. article-jun-3406 status code = 200: The request succeeded, and the resource is returned. article-jun-2920 status code = 200: The request succeeded, and the resource is returned. article-jun-2699 status code = 200: The request succeeded, and the resource is returned. article-jun-0332 status code = 200: The request succeeded, and the resource is returned. article-jun-4500 status code = 200: The request succeeded, and the resource is returned. article-jun-1482 status code = 200: The request succeeded, and the resource is returned. article-jun-0787 status code = 200: The request succeeded, and the resource is returned. article-jun-2022 status code = 200: The request succeeded, and the resource is returned. article-jun-3687 status code = 200: The request succeeded, and the resource is returned. article-jun-3700 status code = 200: The request succeeded, and the resource is returned. article-jun-3528 status code = 200: The request succeeded, and the resource is returned. article-jun-2557 status code = 200: The request succeeded, and the resource is returned. article-jun-2381 status code = 200: The request succeeded, and the resource is returned. article-jun-1633 status code = 200: The request succeeded, and the resource is returned. article-jun-4342 status code = 200: The request succeeded, and the resource is returned. article-jun-4535 status code = 200: The request succeeded, and the resource is returned. article-jun-1155 status code = 200: The request succeeded, and the resource is returned. article-jun-0553 status code = 200: The request succeeded, and the resource is returned. article-jun-1387 status code = 200: The request succeeded, and the resource is returned. article-jun-2401 status code = 200: The request succeeded, and the resource is returned. article-jun-4329 status code = 200: The request succeeded, and the resource is returned. article-jun-1532 status code = 200: The request succeeded, and the resource is returned. article-jun-1434 status code = 200: The request succeeded, and the resource is returned. article-jun-2521 status code = 200: The request succeeded, and the resource is returned. article-jun-2097 status code = 200: The request succeeded, and the resource is returned. article-jun-2226 status code = 200: The request succeeded, and the resource is returned. article-jun-4453 status code = 200: The request succeeded, and the resource is returned. article-jun-4509 status code = 200: The request succeeded, and the resource is returned. article-jun-2467 status code = 200: The request succeeded, and the resource is returned. article-jun-1038 status code = 200: The request succeeded, and the resource is returned. article-jun-2420 status code = 200: The request succeeded, and the resource is returned. article-jun-4994 status code = 200: The request succeeded, and the resource is returned. article-jun-3434 status code = 200: The request succeeded, and the resource is returned. article-jun-4410 status code = 200: The request succeeded, and the resource is returned. article-jun-0925 status code = 200: The request succeeded, and the resource is returned. article-jun-1788 status code = 200: The request succeeded, and the resource is returned. article-jun-0749 status code = 200: The request succeeded, and the resource is returned. article-jun-4657 status code = 200: The request succeeded, and the resource is returned. article-jun-2787 status code = 200: The request succeeded, and the resource is returned. article-jun-2319 status code = 200: The request succeeded, and the resource is returned. article-jun-1141 status code = 200: The request succeeded, and the resource is returned. article-jun-4805 status code = 200: The request succeeded, and the resource is returned. article-jun-0101 status code = 200: The request succeeded, and the resource is returned. article-jun-4636 status code = 200: The request succeeded, and the resource is returned. article-jun-1885 status code = 200: The request succeeded, and the resource is returned. article-jun-1112 status code = 200: The request succeeded, and the resource is returned. article-jun-4470 status code = 200: The request succeeded, and the resource is returned. article-jun-3966 status code = 200: The request succeeded, and the resource is returned. article-jun-1668 status code = 200: The request succeeded, and the resource is returned. article-jun-1940 status code = 200: The request succeeded, and the resource is returned. article-jun-0693 status code = 200: The request succeeded, and the resource is returned. article-jun-0583 status code = 200: The request succeeded, and the resource is returned. article-jun-4042 status code = 200: The request succeeded, and the resource is returned. article-jun-1150 status code = 200: The request succeeded, and the resource is returned. article-jun-0829 status code = 200: The request succeeded, and the resource is returned. article-jun-4361 status code = 200: The request succeeded, and the resource is returned. article-jun-2660 status code = 200: The request succeeded, and the resource is returned. article-jun-3293 status code = 200: The request succeeded, and the resource is returned. article-jun-4523 status code = 200: The request succeeded, and the resource is returned. article-jun-2536 status code = 200: The request succeeded, and the resource is returned. article-jun-0684 status code = 200: The request succeeded, and the resource is returned. article-jun-3126 status code = 200: The request succeeded, and the resource is returned. article-jun-1396 status code = 200: The request succeeded, and the resource is returned. article-jun-3380 status code = 200: The request succeeded, and the resource is returned. article-jun-3599 status code = 200: The request succeeded, and the resource is returned. article-jun-0780 status code = 200: The request succeeded, and the resource is returned. article-jun-0505 status code = 200: The request succeeded, and the resource is returned. article-jun-2320 status code = 200: The request succeeded, and the resource is returned. article-jun-3360 status code = 200: The request succeeded, and the resource is returned. article-jun-1094 status code = 200: The request succeeded, and the resource is returned. article-jun-2505 status code = 200: The request succeeded, and the resource is returned. article-jun-2405 status code = 200: The request succeeded, and the resource is returned. article-jun-4106 status code = 200: The request succeeded, and the resource is returned. article-jun-0039 status code = 200: The request succeeded, and the resource is returned. article-jun-2519 status code = 200: The request succeeded, and the resource is returned. article-jun-1446 status code = 200: The request succeeded, and the resource is returned. article-jun-1293 status code = 200: The request succeeded, and the resource is returned. article-jun-4259 status code = 200: The request succeeded, and the resource is returned. article-jun-4520 status code = 200: The request succeeded, and the resource is returned. article-jul-1745 status code = 200: The request succeeded, and the resource is returned. article-jul-0465 status code = 200: The request succeeded, and the resource is returned. article-jul-4512 status code = 200: The request succeeded, and the resource is returned. article-jul-2758 status code = 200: The request succeeded, and the resource is returned. article-jul-4068 status code = 200: The request succeeded, and the resource is returned. article-jul-1257 status code = 200: The request succeeded, and the resource is returned. article-jul-2965 status code = 200: The request succeeded, and the resource is returned. article-jul-2977 status code = 200: The request succeeded, and the resource is returned. article-jul-0794 status code = 200: The request succeeded, and the resource is returned. article-jul-0495 status code = 200: The request succeeded, and the resource is returned. article-jul-2215 status code = 200: The request succeeded, and the resource is returned. article-jul-4187 status code = 200: The request succeeded, and the resource is returned. article-jul-3607 status code = 200: The request succeeded, and the resource is returned. article-jul-3406 status code = 200: The request succeeded, and the resource is returned. article-jul-4480 status code = 200: The request succeeded, and the resource is returned. article-jul-4575 status code = 200: The request succeeded, and the resource is returned. article-jul-2829 status code = 200: The request succeeded, and the resource is returned. article-jul-4356 status code = 200: The request succeeded, and the resource is returned. article-jul-3487 status code = 200: The request succeeded, and the resource is returned. article-jul-3941 status code = 200: The request succeeded, and the resource is returned. article-jul-4527 status code = 200: The request succeeded, and the resource is returned. article-jul-3516 status code = 200: The request succeeded, and the resource is returned. article-jul-0165 status code = 200: The request succeeded, and the resource is returned. article-jul-2340 status code = 200: The request succeeded, and the resource is returned. article-jul-4614 status code = 200: The request succeeded, and the resource is returned. article-jul-2009 status code = 200: The request succeeded, and the resource is returned. article-jul-2742 status code = 200: The request succeeded, and the resource is returned. article-jul-3010 status code = 200: The request succeeded, and the resource is returned. article-jul-4101 status code = 200: The request succeeded, and the resource is returned. article-jul-2708 status code = 200: The request succeeded, and the resource is returned. article-jul-4268 status code = 200: The request succeeded, and the resource is returned. article-jul-4019 status code = 200: The request succeeded, and the resource is returned. article-jul-1748 status code = 200: The request succeeded, and the resource is returned. article-jul-1249 status code = 200: The request succeeded, and the resource is returned. article-jul-4170 status code = 200: The request succeeded, and the resource is returned. article-jul-2162 status code = 200: The request succeeded, and the resource is returned. article-jul-0339 status code = 200: The request succeeded, and the resource is returned. article-jul-2286 status code = 200: The request succeeded, and the resource is returned. article-jul-2988 status code = 200: The request succeeded, and the resource is returned. article-jul-4021 status code = 200: The request succeeded, and the resource is returned. article-jul-4373 status code = 200: The request succeeded, and the resource is returned. article-jul-4895 status code = 200: The request succeeded, and the resource is returned. article-jul-4326 status code = 200: The request succeeded, and the resource is returned. article-jul-0662 status code = 200: The request succeeded, and the resource is returned. article-jul-2558 status code = 200: The request succeeded, and the resource is returned. article-jul-3455 status code = 200: The request succeeded, and the resource is returned. article-jul-4969 status code = 200: The request succeeded, and the resource is returned. article-jul-3422 status code = 200: The request succeeded, and the resource is returned. article-jul-1711 status code = 200: The request succeeded, and the resource is returned. article-jul-2063 status code = 200: The request succeeded, and the resource is returned. article-jul-2960 status code = 200: The request succeeded, and the resource is returned. article-jul-4915 status code = 200: The request succeeded, and the resource is returned. article-jul-4812 status code = 200: The request succeeded, and the resource is returned. article-jul-3520 status code = 200: The request succeeded, and the resource is returned. article-jul-0643 status code = 200: The request succeeded, and the resource is returned. article-jul-3273 status code = 200: The request succeeded, and the resource is returned. article-jul-3870 status code = 200: The request succeeded, and the resource is returned. article-jul-0803 status code = 200: The request succeeded, and the resource is returned. article-jul-4157 status code = 200: The request succeeded, and the resource is returned. article-jul-0297 status code = 200: The request succeeded, and the resource is returned. article-jul-4908 status code = 200: The request succeeded, and the resource is returned. article-jul-1013 status code = 200: The request succeeded, and the resource is returned. article-jul-4496 status code = 200: The request succeeded, and the resource is returned. article-jul-3168 status code = 200: The request succeeded, and the resource is returned. article-jul-4242 status code = 200: The request succeeded, and the resource is returned. article-jul-2527 status code = 200: The request succeeded, and the resource is returned. article-jul-1760 status code = 200: The request succeeded, and the resource is returned. article-jul-4400 status code = 200: The request succeeded, and the resource is returned. article-jul-4827 status code = 200: The request succeeded, and the resource is returned. article-jul-4341 status code = 200: The request succeeded, and the resource is returned. article-jul-3525 status code = 200: The request succeeded, and the resource is returned. article-jul-2544 status code = 200: The request succeeded, and the resource is returned. article-jul-0637 status code = 200: The request succeeded, and the resource is returned. article-jul-4798 status code = 200: The request succeeded, and the resource is returned. article-jul-2909 status code = 200: The request succeeded, and the resource is returned. article-jul-2815 status code = 200: The request succeeded, and the resource is returned. article-jul-4628 status code = 200: The request succeeded, and the resource is returned. article-jul-1361 status code = 200: The request succeeded, and the resource is returned. article-jul-0597 status code = 200: The request succeeded, and the resource is returned. article-jul-2241 status code = 200: The request succeeded, and the resource is returned. article-jul-1728 status code = 200: The request succeeded, and the resource is returned. article-jul-1219 status code = 200: The request succeeded, and the resource is returned. article-jul-4829 status code = 200: The request succeeded, and the resource is returned. article-jul-1320 status code = 200: The request succeeded, and the resource is returned. article-jul-2463 status code = 200: The request succeeded, and the resource is returned. article-jul-3438 status code = 200: The request succeeded, and the resource is returned. article-jul-4719 status code = 200: The request succeeded, and the resource is returned. article-jul-3289 status code = 200: The request succeeded, and the resource is returned. article-jul-2439 status code = 200: The request succeeded, and the resource is returned. article-jul-3943 status code = 200: The request succeeded, and the resource is returned. article-jul-3331 status code = 200: The request succeeded, and the resource is returned. article-jul-3957 status code = 200: The request succeeded, and the resource is returned. article-jul-0733 status code = 200: The request succeeded, and the resource is returned. article-jul-0168 status code = 200: The request succeeded, and the resource is returned. article-jul-2667 status code = 200: The request succeeded, and the resource is returned. article-jul-4244 status code = 200: The request succeeded, and the resource is returned. article-jul-0334 status code = 200: The request succeeded, and the resource is returned. article-jul-0645 status code = 200: The request succeeded, and the resource is returned. article-jul-3380 status code = 200: The request succeeded, and the resource is returned. article-jul-0019 status code = 200: The request succeeded, and the resource is returned. article-jul-4790 status code = 200: The request succeeded, and the resource is returned. article-jul-4384 status code = 200: The request succeeded, and the resource is returned. article-jul-3522 status code = 200: The request succeeded, and the resource is returned. article-jul-1717 status code = 200: The request succeeded, and the resource is returned. article-jul-1112 status code = 200: The request succeeded, and the resource is returned. article-jul-0202 status code = 200: The request succeeded, and the resource is returned. article-jul-1885 status code = 200: The request succeeded, and the resource is returned. article-jul-3830 status code = 200: The request succeeded, and the resource is returned. article-jul-0931 status code = 200: The request succeeded, and the resource is returned. article-jul-1059 status code = 200: The request succeeded, and the resource is returned. article-jul-3879 status code = 200: The request succeeded, and the resource is returned. article-jul-4233 status code = 200: The request succeeded, and the resource is returned. article-jul-0417 status code = 200: The request succeeded, and the resource is returned. article-jul-3332 status code = 200: The request succeeded, and the resource is returned. article-jul-0589 status code = 200: The request succeeded, and the resource is returned. article-jul-4193 status code = 200: The request succeeded, and the resource is returned. article-jul-0503 status code = 200: The request succeeded, and the resource is returned. article-jul-1350 status code = 200: The request succeeded, and the resource is returned. article-jul-1729 status code = 200: The request succeeded, and the resource is returned. article-jul-4500 status code = 200: The request succeeded, and the resource is returned. article-jul-3874 status code = 200: The request succeeded, and the resource is returned. article-jul-2168 status code = 200: The request succeeded, and the resource is returned. article-aug-3515 status code = 200: The request succeeded, and the resource is returned. article-aug-1123 status code = 200: The request succeeded, and the resource is returned. article-aug-4475 status code = 200: The request succeeded, and the resource is returned. article-aug-3384 status code = 200: The request succeeded, and the resource is returned. article-aug-4019 status code = 200: The request succeeded, and the resource is returned. article-aug-0806 status code = 200: The request succeeded, and the resource is returned. article-aug-4964 status code = 200: The request succeeded, and the resource is returned. article-aug-4265 status code = 200: The request succeeded, and the resource is returned. article-aug-3926 status code = 200: The request succeeded, and the resource is returned. article-aug-4614 status code = 200: The request succeeded, and the resource is returned. article-aug-2567 status code = 200: The request succeeded, and the resource is returned. article-aug-4701 status code = 200: The request succeeded, and the resource is returned. article-aug-0893 status code = 200: The request succeeded, and the resource is returned. article-aug-4668 status code = 200: The request succeeded, and the resource is returned. article-aug-1637 status code = 200: The request succeeded, and the resource is returned. article-aug-0324 status code = 200: The request succeeded, and the resource is returned. article-aug-0908 status code = 200: The request succeeded, and the resource is returned. article-aug-1443 status code = 200: The request succeeded, and the resource is returned. article-aug-3079 status code = 200: The request succeeded, and the resource is returned. article-aug-0896 status code = 200: The request succeeded, and the resource is returned. article-aug-4896 status code = 200: The request succeeded, and the resource is returned. article-aug-3579 status code = 200: The request succeeded, and the resource is returned. article-aug-4658 status code = 200: The request succeeded, and the resource is returned. article-aug-2737 status code = 200: The request succeeded, and the resource is returned. article-aug-1784 status code = 200: The request succeeded, and the resource is returned. article-aug-2063 status code = 200: The request succeeded, and the resource is returned. article-aug-1532 status code = 200: The request succeeded, and the resource is returned. article-aug-0644 status code = 200: The request succeeded, and the resource is returned. article-aug-3247 status code = 200: The request succeeded, and the resource is returned. article-aug-2464 status code = 200: The request succeeded, and the resource is returned. article-aug-2142 status code = 200: The request succeeded, and the resource is returned. article-aug-1157 status code = 200: The request succeeded, and the resource is returned. article-aug-2119 status code = 200: The request succeeded, and the resource is returned. article-aug-3859 status code = 200: The request succeeded, and the resource is returned. article-aug-1614 status code = 200: The request succeeded, and the resource is returned. article-aug-3206 status code = 200: The request succeeded, and the resource is returned. article-aug-3335 status code = 200: The request succeeded, and the resource is returned. article-aug-2889 status code = 200: The request succeeded, and the resource is returned. article-aug-0763 status code = 200: The request succeeded, and the resource is returned. article-aug-0648 status code = 200: The request succeeded, and the resource is returned. article-aug-1226 status code = 200: The request succeeded, and the resource is returned. article-aug-2762 status code = 200: The request succeeded, and the resource is returned. article-aug-0319 status code = 200: The request succeeded, and the resource is returned. article-aug-3240 status code = 200: The request succeeded, and the resource is returned. article-aug-1817 status code = 200: The request succeeded, and the resource is returned. article-aug-0781 status code = 200: The request succeeded, and the resource is returned. article-aug-4887 status code = 200: The request succeeded, and the resource is returned. article-aug-4692 status code = 200: The request succeeded, and the resource is returned. article-aug-0685 status code = 200: The request succeeded, and the resource is returned. article-aug-1101 status code = 200: The request succeeded, and the resource is returned. article-aug-1823 status code = 200: The request succeeded, and the resource is returned. article-aug-3199 status code = 200: The request succeeded, and the resource is returned. article-aug-0176 status code = 200: The request succeeded, and the resource is returned. article-aug-1452 status code = 200: The request succeeded, and the resource is returned. article-aug-3112 status code = 200: The request succeeded, and the resource is returned. article-aug-0373 status code = 200: The request succeeded, and the resource is returned. article-aug-4936 status code = 200: The request succeeded, and the resource is returned. article-aug-1558 status code = 200: The request succeeded, and the resource is returned. article-aug-3754 status code = 200: The request succeeded, and the resource is returned. article-aug-2783 status code = 200: The request succeeded, and the resource is returned. article-aug-4587 status code = 200: The request succeeded, and the resource is returned. article-aug-3962 status code = 200: The request succeeded, and the resource is returned. article-aug-0665 status code = 200: The request succeeded, and the resource is returned. article-aug-3312 status code = 200: The request succeeded, and the resource is returned. article-aug-2549 status code = 200: The request succeeded, and the resource is returned. article-aug-4009 status code = 200: The request succeeded, and the resource is returned. article-aug-2180 status code = 200: The request succeeded, and the resource is returned. article-aug-3857 status code = 200: The request succeeded, and the resource is returned. article-aug-3877 status code = 200: The request succeeded, and the resource is returned. article-aug-3101 status code = 200: The request succeeded, and the resource is returned. article-aug-3408 status code = 200: The request succeeded, and the resource is returned. article-aug-3126 status code = 200: The request succeeded, and the resource is returned. article-aug-2688 status code = 200: The request succeeded, and the resource is returned. article-aug-2601 status code = 200: The request succeeded, and the resource is returned. article-aug-1976 status code = 200: The request succeeded, and the resource is returned. article-aug-4015 status code = 200: The request succeeded, and the resource is returned. article-aug-4910 status code = 200: The request succeeded, and the resource is returned. article-aug-1990 status code = 200: The request succeeded, and the resource is returned. article-aug-3360 status code = 200: The request succeeded, and the resource is returned. article-aug-3213 status code = 200: The request succeeded, and the resource is returned. article-aug-3564 status code = 200: The request succeeded, and the resource is returned. article-aug-4998 status code = 200: The request succeeded, and the resource is returned. article-aug-0801 status code = 200: The request succeeded, and the resource is returned. article-aug-4456 status code = 200: The request succeeded, and the resource is returned. article-aug-4931 status code = 200: The request succeeded, and the resource is returned. article-aug-4232 status code = 200: The request succeeded, and the resource is returned. article-aug-1046 status code = 200: The request succeeded, and the resource is returned. article-aug-3940 status code = 200: The request succeeded, and the resource is returned. article-aug-4740 status code = 200: The request succeeded, and the resource is returned. article-aug-2812 status code = 200: The request succeeded, and the resource is returned. article-aug-0633 status code = 200: The request succeeded, and the resource is returned. article-aug-3280 status code = 200: The request succeeded, and the resource is returned. article-aug-4746 status code = 200: The request succeeded, and the resource is returned. article-aug-4220 status code = 200: The request succeeded, and the resource is returned. article-aug-1720 status code = 200: The request succeeded, and the resource is returned. article-aug-1390 status code = 200: The request succeeded, and the resource is returned. article-aug-2437 status code = 200: The request succeeded, and the resource is returned. article-aug-1486 status code = 200: The request succeeded, and the resource is returned. article-aug-2512 status code = 200: The request succeeded, and the resource is returned. article-aug-1933 status code = 200: The request succeeded, and the resource is returned. article-aug-1337 status code = 200: The request succeeded, and the resource is returned. article-aug-4172 status code = 200: The request succeeded, and the resource is returned. article-aug-3301 status code = 200: The request succeeded, and the resource is returned. article-aug-2517 status code = 200: The request succeeded, and the resource is returned. article-aug-4672 status code = 200: The request succeeded, and the resource is returned. article-aug-0800 status code = 200: The request succeeded, and the resource is returned. article-aug-0840 status code = 200: The request succeeded, and the resource is returned. article-aug-4831 status code = 200: The request succeeded, and the resource is returned. article-aug-2563 status code = 200: The request succeeded, and the resource is returned. article-aug-4625 status code = 200: The request succeeded, and the resource is returned. article-aug-1797 status code = 200: The request succeeded, and the resource is returned. article-aug-2487 status code = 200: The request succeeded, and the resource is returned. article-aug-0053 status code = 200: The request succeeded, and the resource is returned. article-aug-1767 status code = 200: The request succeeded, and the resource is returned. article-aug-3362 status code = 200: The request succeeded, and the resource is returned. article-aug-1146 status code = 200: The request succeeded, and the resource is returned. article-sep-1375 status code = 200: The request succeeded, and the resource is returned. article-sep-3874 status code = 200: The request succeeded, and the resource is returned. article-sep-0096 status code = 200: The request succeeded, and the resource is returned. article-sep-3814 status code = 200: The request succeeded, and the resource is returned. article-sep-4619 status code = 200: The request succeeded, and the resource is returned. article-sep-3639 status code = 200: The request succeeded, and the resource is returned. article-sep-4996 status code = 200: The request succeeded, and the resource is returned. article-sep-3370 status code = 200: The request succeeded, and the resource is returned. article-sep-0503 status code = 200: The request succeeded, and the resource is returned. article-sep-4426 status code = 200: The request succeeded, and the resource is returned. article-sep-1785 status code = 200: The request succeeded, and the resource is returned. article-sep-3918 status code = 200: The request succeeded, and the resource is returned. article-sep-0401 status code = 200: The request succeeded, and the resource is returned. article-sep-2816 status code = 200: The request succeeded, and the resource is returned. article-sep-3016 status code = 200: The request succeeded, and the resource is returned. article-sep-2767 status code = 200: The request succeeded, and the resource is returned. article-sep-4852 status code = 200: The request succeeded, and the resource is returned. article-sep-1803 status code = 200: The request succeeded, and the resource is returned. article-sep-0386 status code = 200: The request succeeded, and the resource is returned. article-sep-3587 status code = 200: The request succeeded, and the resource is returned. article-sep-1349 status code = 200: The request succeeded, and the resource is returned. article-sep-3570 status code = 200: The request succeeded, and the resource is returned. article-sep-4970 status code = 200: The request succeeded, and the resource is returned. article-sep-0920 status code = 200: The request succeeded, and the resource is returned. article-sep-4665 status code = 200: The request succeeded, and the resource is returned. article-sep-3053 status code = 200: The request succeeded, and the resource is returned. article-sep-4093 status code = 200: The request succeeded, and the resource is returned. article-sep-1145 status code = 200: The request succeeded, and the resource is returned. article-sep-3077 status code = 200: The request succeeded, and the resource is returned. article-sep-2562 status code = 200: The request succeeded, and the resource is returned. article-sep-4352 status code = 200: The request succeeded, and the resource is returned. article-sep-2859 status code = 200: The request succeeded, and the resource is returned. article-sep-3900 status code = 200: The request succeeded, and the resource is returned. article-sep-3760 status code = 200: The request succeeded, and the resource is returned. article-sep-2440 status code = 200: The request succeeded, and the resource is returned. article-sep-2872 status code = 200: The request succeeded, and the resource is returned. article-sep-4414 status code = 200: The request succeeded, and the resource is returned. article-sep-4877 status code = 200: The request succeeded, and the resource is returned. article-sep-3592 status code = 200: The request succeeded, and the resource is returned. article-sep-0022 status code = 200: The request succeeded, and the resource is returned. article-sep-0232 status code = 200: The request succeeded, and the resource is returned. article-sep-1748 status code = 200: The request succeeded, and the resource is returned. article-sep-4345 status code = 200: The request succeeded, and the resource is returned. article-sep-1443 status code = 200: The request succeeded, and the resource is returned. article-sep-4275 status code = 200: The request succeeded, and the resource is returned. article-sep-0555 status code = 200: The request succeeded, and the resource is returned. article-sep-4613 status code = 200: The request succeeded, and the resource is returned. article-sep-2949 status code = 200: The request succeeded, and the resource is returned. article-sep-3287 status code = 200: The request succeeded, and the resource is returned. article-sep-4860 status code = 200: The request succeeded, and the resource is returned. article-sep-3046 status code = 200: The request succeeded, and the resource is returned. article-sep-4890 status code = 200: The request succeeded, and the resource is returned. article-sep-3678 status code = 200: The request succeeded, and the resource is returned. article-sep-3602 status code = 200: The request succeeded, and the resource is returned. article-sep-4876 status code = 200: The request succeeded, and the resource is returned. article-sep-4207 status code = 200: The request succeeded, and the resource is returned. article-sep-1238 status code = 200: The request succeeded, and the resource is returned. article-sep-4867 status code = 200: The request succeeded, and the resource is returned. article-sep-0823 status code = 200: The request succeeded, and the resource is returned. article-sep-0254 status code = 200: The request succeeded, and the resource is returned. article-sep-3264 status code = 200: The request succeeded, and the resource is returned. article-sep-2587 status code = 200: The request succeeded, and the resource is returned. article-sep-4095 status code = 200: The request succeeded, and the resource is returned. article-sep-1406 status code = 200: The request succeeded, and the resource is returned. article-sep-2272 status code = 200: The request succeeded, and the resource is returned. article-sep-0584 status code = 200: The request succeeded, and the resource is returned. article-sep-4404 status code = 200: The request succeeded, and the resource is returned. article-sep-1405 status code = 200: The request succeeded, and the resource is returned. article-sep-2520 status code = 200: The request succeeded, and the resource is returned. article-sep-4936 status code = 200: The request succeeded, and the resource is returned. article-sep-1696 status code = 200: The request succeeded, and the resource is returned. article-sep-3711 status code = 200: The request succeeded, and the resource is returned. article-sep-3745 status code = 200: The request succeeded, and the resource is returned. article-sep-0082 status code = 200: The request succeeded, and the resource is returned. article-sep-3128 status code = 200: The request succeeded, and the resource is returned. article-sep-4623 status code = 200: The request succeeded, and the resource is returned. article-sep-0592 status code = 200: The request succeeded, and the resource is returned. article-sep-1313 status code = 200: The request succeeded, and the resource is returned. article-sep-1672 status code = 200: The request succeeded, and the resource is returned. article-sep-0863 status code = 200: The request succeeded, and the resource is returned. article-sep-1411 status code = 200: The request succeeded, and the resource is returned. article-sep-3583 status code = 200: The request succeeded, and the resource is returned. article-sep-2426 status code = 200: The request succeeded, and the resource is returned. article-sep-3558 status code = 200: The request succeeded, and the resource is returned. article-sep-1539 status code = 200: The request succeeded, and the resource is returned. article-sep-3347 status code = 200: The request succeeded, and the resource is returned. article-sep-2082 status code = 200: The request succeeded, and the resource is returned. article-sep-4027 status code = 200: The request succeeded, and the resource is returned. article-sep-4329 status code = 200: The request succeeded, and the resource is returned. article-sep-4851 status code = 200: The request succeeded, and the resource is returned. article-sep-1519 status code = 200: The request succeeded, and the resource is returned. article-sep-1782 status code = 200: The request succeeded, and the resource is returned. article-sep-4130 status code = 200: The request succeeded, and the resource is returned. article-sep-2601 status code = 200: The request succeeded, and the resource is returned. article-sep-0226 status code = 200: The request succeeded, and the resource is returned. article-sep-2914 status code = 200: The request succeeded, and the resource is returned. article-sep-0174 status code = 200: The request succeeded, and the resource is returned. article-sep-4443 status code = 200: The request succeeded, and the resource is returned. article-sep-0063 status code = 200: The request succeeded, and the resource is returned. article-sep-4940 status code = 200: The request succeeded, and the resource is returned. article-sep-2528 status code = 200: The request succeeded, and the resource is returned. article-sep-4107 status code = 200: The request succeeded, and the resource is returned. article-sep-2374 status code = 200: The request succeeded, and the resource is returned. article-sep-3813 status code = 200: The request succeeded, and the resource is returned. article-sep-2892 status code = 200: The request succeeded, and the resource is returned. article-sep-0388 status code = 200: The request succeeded, and the resource is returned. article-sep-3414 status code = 200: The request succeeded, and the resource is returned. article-sep-2293 status code = 200: The request succeeded, and the resource is returned. article-sep-1752 status code = 200: The request succeeded, and the resource is returned. article-sep-2040 status code = 200: The request succeeded, and the resource is returned. article-sep-0237 status code = 200: The request succeeded, and the resource is returned. article-sep-2517 status code = 200: The request succeeded, and the resource is returned. article-oct-3861 status code = 200: The request succeeded, and the resource is returned. article-oct-0443 status code = 200: The request succeeded, and the resource is returned. article-oct-0777 status code = 200: The request succeeded, and the resource is returned. article-oct-1914 status code = 200: The request succeeded, and the resource is returned. article-oct-2035 status code = 200: The request succeeded, and the resource is returned. article-oct-0709 status code = 200: The request succeeded, and the resource is returned. article-oct-1226 status code = 200: The request succeeded, and the resource is returned. article-oct-0945 status code = 200: The request succeeded, and the resource is returned. article-oct-1295 status code = 200: The request succeeded, and the resource is returned. article-oct-4199 status code = 200: The request succeeded, and the resource is returned. article-oct-0924 status code = 200: The request succeeded, and the resource is returned. article-oct-3880 status code = 200: The request succeeded, and the resource is returned. article-oct-3790 status code = 200: The request succeeded, and the resource is returned. article-oct-3326 status code = 200: The request succeeded, and the resource is returned. article-oct-1924 status code = 200: The request succeeded, and the resource is returned. article-oct-3040 status code = 200: The request succeeded, and the resource is returned. article-oct-1787 status code = 200: The request succeeded, and the resource is returned. article-oct-2701 status code = 200: The request succeeded, and the resource is returned. article-oct-2317 status code = 200: The request succeeded, and the resource is returned. article-oct-0300 status code = 200: The request succeeded, and the resource is returned. article-oct-4879 status code = 200: The request succeeded, and the resource is returned. article-oct-0163 status code = 200: The request succeeded, and the resource is returned. article-oct-4089 status code = 200: The request succeeded, and the resource is returned. article-oct-4812 status code = 200: The request succeeded, and the resource is returned. article-oct-4850 status code = 200: The request succeeded, and the resource is returned. article-oct-3470 status code = 200: The request succeeded, and the resource is returned. article-oct-4758 status code = 200: The request succeeded, and the resource is returned. article-oct-2145 status code = 200: The request succeeded, and the resource is returned. article-oct-3548 status code = 200: The request succeeded, and the resource is returned. article-oct-3256 status code = 200: The request succeeded, and the resource is returned. article-oct-4836 status code = 200: The request succeeded, and the resource is returned. article-oct-2748 status code = 200: The request succeeded, and the resource is returned. article-oct-0832 status code = 200: The request succeeded, and the resource is returned. article-oct-2828 status code = 200: The request succeeded, and the resource is returned. article-oct-0817 status code = 200: The request succeeded, and the resource is returned. article-oct-2348 status code = 200: The request succeeded, and the resource is returned. article-oct-1261 status code = 200: The request succeeded, and the resource is returned. article-oct-0898 status code = 200: The request succeeded, and the resource is returned. article-oct-1441 status code = 200: The request succeeded, and the resource is returned. article-oct-0064 status code = 200: The request succeeded, and the resource is returned. article-oct-2801 status code = 200: The request succeeded, and the resource is returned. article-oct-2209 status code = 200: The request succeeded, and the resource is returned. article-oct-2955 status code = 200: The request succeeded, and the resource is returned. article-oct-3459 status code = 200: The request succeeded, and the resource is returned. article-oct-4140 status code = 200: The request succeeded, and the resource is returned. article-oct-4990 status code = 200: The request succeeded, and the resource is returned. article-oct-3207 status code = 200: The request succeeded, and the resource is returned. article-oct-0844 status code = 200: The request succeeded, and the resource is returned. article-oct-2177 status code = 200: The request succeeded, and the resource is returned. article-oct-3103 status code = 200: The request succeeded, and the resource is returned. article-oct-4815 status code = 200: The request succeeded, and the resource is returned. article-oct-4038 status code = 200: The request succeeded, and the resource is returned. article-oct-1049 status code = 200: The request succeeded, and the resource is returned. article-oct-1011 status code = 200: The request succeeded, and the resource is returned. article-oct-0417 status code = 200: The request succeeded, and the resource is returned. article-oct-3162 status code = 200: The request succeeded, and the resource is returned. article-oct-0421 status code = 200: The request succeeded, and the resource is returned. article-oct-2333 status code = 200: The request succeeded, and the resource is returned. article-oct-4803 status code = 200: The request succeeded, and the resource is returned. article-oct-1193 status code = 200: The request succeeded, and the resource is returned. article-oct-4546 status code = 200: The request succeeded, and the resource is returned. article-oct-0057 status code = 200: The request succeeded, and the resource is returned. article-oct-4981 status code = 200: The request succeeded, and the resource is returned. article-oct-4914 status code = 200: The request succeeded, and the resource is returned. article-oct-1753 status code = 200: The request succeeded, and the resource is returned. article-oct-2102 status code = 200: The request succeeded, and the resource is returned. article-oct-2774 status code = 200: The request succeeded, and the resource is returned. article-oct-4605 status code = 200: The request succeeded, and the resource is returned. article-oct-3910 status code = 200: The request succeeded, and the resource is returned. article-oct-2454 status code = 200: The request succeeded, and the resource is returned. article-oct-0059 status code = 200: The request succeeded, and the resource is returned. article-oct-0992 status code = 200: The request succeeded, and the resource is returned. article-oct-2740 status code = 200: The request succeeded, and the resource is returned. article-oct-2886 status code = 200: The request succeeded, and the resource is returned. article-oct-4166 status code = 200: The request succeeded, and the resource is returned. article-oct-2188 status code = 200: The request succeeded, and the resource is returned. article-oct-4084 status code = 200: The request succeeded, and the resource is returned. article-oct-2574 status code = 200: The request succeeded, and the resource is returned. article-oct-3663 status code = 200: The request succeeded, and the resource is returned. article-oct-2181 status code = 200: The request succeeded, and the resource is returned. article-oct-0999 status code = 200: The request succeeded, and the resource is returned. article-oct-2121 status code = 200: The request succeeded, and the resource is returned. article-oct-2094 status code = 200: The request succeeded, and the resource is returned. article-oct-2301 status code = 200: The request succeeded, and the resource is returned. article-oct-0815 status code = 200: The request succeeded, and the resource is returned. article-oct-3222 status code = 200: The request succeeded, and the resource is returned. article-oct-4804 status code = 200: The request succeeded, and the resource is returned. article-oct-1215 status code = 200: The request succeeded, and the resource is returned. article-oct-2080 status code = 200: The request succeeded, and the resource is returned. article-oct-0412 status code = 200: The request succeeded, and the resource is returned. article-oct-3387 status code = 200: The request succeeded, and the resource is returned. article-oct-3524 status code = 200: The request succeeded, and the resource is returned. article-oct-0580 status code = 200: The request succeeded, and the resource is returned. article-oct-0746 status code = 200: The request succeeded, and the resource is returned. article-oct-1921 status code = 200: The request succeeded, and the resource is returned. article-oct-0382 status code = 200: The request succeeded, and the resource is returned. article-oct-1007 status code = 200: The request succeeded, and the resource is returned. article-oct-3749 status code = 200: The request succeeded, and the resource is returned. article-oct-3029 status code = 200: The request succeeded, and the resource is returned. article-oct-0721 status code = 200: The request succeeded, and the resource is returned. article-oct-0108 status code = 200: The request succeeded, and the resource is returned. article-oct-3547 status code = 200: The request succeeded, and the resource is returned. article-oct-0113 status code = 200: The request succeeded, and the resource is returned. article-oct-0512 status code = 200: The request succeeded, and the resource is returned. article-oct-1276 status code = 200: The request succeeded, and the resource is returned. article-oct-0911 status code = 200: The request succeeded, and the resource is returned. article-oct-2527 status code = 200: The request succeeded, and the resource is returned. article-oct-0621 status code = 200: The request succeeded, and the resource is returned. article-oct-1756 status code = 200: The request succeeded, and the resource is returned. article-oct-3563 status code = 200: The request succeeded, and the resource is returned. article-oct-3862 status code = 200: The request succeeded, and the resource is returned. article-oct-2448 status code = 200: The request succeeded, and the resource is returned. article-oct-1751 status code = 200: The request succeeded, and the resource is returned. article-oct-1516 status code = 200: The request succeeded, and the resource is returned. article-oct-3160 status code = 200: The request succeeded, and the resource is returned. article-oct-0660 status code = 200: The request succeeded, and the resource is returned. article-oct-2835 status code = 200: The request succeeded, and the resource is returned. article-oct-4649 status code = 200: The request succeeded, and the resource is returned. article-oct-3920 status code = 200: The request succeeded, and the resource is returned. article-oct-2293 status code = 200: The request succeeded, and the resource is returned. article-oct-2189 status code = 200: The request succeeded, and the resource is returned. article-oct-4749 status code = 200: The request succeeded, and the resource is returned. article-nov-3410 status code = 200: The request succeeded, and the resource is returned. article-nov-3432 status code = 200: The request succeeded, and the resource is returned. article-nov-3526 status code = 200: The request succeeded, and the resource is returned. article-nov-1412 status code = 200: The request succeeded, and the resource is returned. article-nov-2347 status code = 200: The request succeeded, and the resource is returned. article-nov-2329 status code = 200: The request succeeded, and the resource is returned. article-nov-0186 status code = 200: The request succeeded, and the resource is returned. article-nov-0386 status code = 200: The request succeeded, and the resource is returned. article-nov-1815 status code = 200: The request succeeded, and the resource is returned. article-nov-3071 status code = 200: The request succeeded, and the resource is returned. article-nov-3512 status code = 200: The request succeeded, and the resource is returned. article-nov-3037 status code = 200: The request succeeded, and the resource is returned. article-nov-4414 status code = 200: The request succeeded, and the resource is returned. article-nov-4953 status code = 200: The request succeeded, and the resource is returned. article-nov-2298 status code = 200: The request succeeded, and the resource is returned. article-nov-3205 status code = 200: The request succeeded, and the resource is returned. article-nov-0449 status code = 200: The request succeeded, and the resource is returned. article-nov-4384 status code = 200: The request succeeded, and the resource is returned. article-nov-4904 status code = 200: The request succeeded, and the resource is returned. article-nov-3590 status code = 200: The request succeeded, and the resource is returned. article-nov-3584 status code = 200: The request succeeded, and the resource is returned. article-nov-4254 status code = 200: The request succeeded, and the resource is returned. article-nov-2334 status code = 200: The request succeeded, and the resource is returned. article-nov-2909 status code = 200: The request succeeded, and the resource is returned. article-nov-3579 status code = 200: The request succeeded, and the resource is returned. article-nov-2737 status code = 200: The request succeeded, and the resource is returned. article-nov-4176 status code = 200: The request succeeded, and the resource is returned. article-nov-1674 status code = 200: The request succeeded, and the resource is returned. article-nov-1801 status code = 200: The request succeeded, and the resource is returned. article-nov-4606 status code = 200: The request succeeded, and the resource is returned. article-nov-2647 status code = 200: The request succeeded, and the resource is returned. article-nov-3091 status code = 200: The request succeeded, and the resource is returned. article-nov-2546 status code = 200: The request succeeded, and the resource is returned. article-nov-4461 status code = 200: The request succeeded, and the resource is returned. article-nov-2714 status code = 200: The request succeeded, and the resource is returned. article-nov-2130 status code = 200: The request succeeded, and the resource is returned. article-nov-3500 status code = 200: The request succeeded, and the resource is returned. article-nov-1451 status code = 200: The request succeeded, and the resource is returned. article-nov-0372 status code = 200: The request succeeded, and the resource is returned. article-nov-4242 status code = 200: The request succeeded, and the resource is returned. article-nov-2953 status code = 200: The request succeeded, and the resource is returned. article-nov-1329 status code = 200: The request succeeded, and the resource is returned. article-nov-2527 status code = 200: The request succeeded, and the resource is returned. article-nov-3879 status code = 200: The request succeeded, and the resource is returned. article-nov-4060 status code = 200: The request succeeded, and the resource is returned. article-nov-3626 status code = 200: The request succeeded, and the resource is returned. article-nov-3702 status code = 200: The request succeeded, and the resource is returned. article-nov-0130 status code = 200: The request succeeded, and the resource is returned. article-nov-4260 status code = 200: The request succeeded, and the resource is returned. article-nov-3907 status code = 200: The request succeeded, and the resource is returned. article-nov-0746 status code = 200: The request succeeded, and the resource is returned. article-nov-0232 status code = 200: The request succeeded, and the resource is returned. article-nov-1428 status code = 200: The request succeeded, and the resource is returned. article-nov-1085 status code = 200: The request succeeded, and the resource is returned. article-nov-1407 status code = 200: The request succeeded, and the resource is returned. article-nov-3342 status code = 200: The request succeeded, and the resource is returned. article-nov-4180 status code = 200: The request succeeded, and the resource is returned. article-nov-1466 status code = 200: The request succeeded, and the resource is returned. article-nov-4049 status code = 200: The request succeeded, and the resource is returned. article-nov-4897 status code = 200: The request succeeded, and the resource is returned. article-nov-0748 status code = 200: The request succeeded, and the resource is returned. article-nov-1277 status code = 200: The request succeeded, and the resource is returned. article-nov-0580 status code = 200: The request succeeded, and the resource is returned. article-nov-3296 status code = 200: The request succeeded, and the resource is returned. article-nov-4356 status code = 200: The request succeeded, and the resource is returned. article-nov-3617 status code = 200: The request succeeded, and the resource is returned. article-nov-1260 status code = 200: The request succeeded, and the resource is returned. article-nov-0628 status code = 200: The request succeeded, and the resource is returned. article-nov-0654 status code = 200: The request succeeded, and the resource is returned. article-nov-0518 status code = 200: The request succeeded, and the resource is returned. article-nov-2968 status code = 200: The request succeeded, and the resource is returned. article-nov-3867 status code = 200: The request succeeded, and the resource is returned. article-nov-0420 status code = 200: The request succeeded, and the resource is returned. article-nov-4250 status code = 200: The request succeeded, and the resource is returned. article-nov-4495 status code = 200: The request succeeded, and the resource is returned. article-nov-2769 status code = 200: The request succeeded, and the resource is returned. article-nov-1623 status code = 200: The request succeeded, and the resource is returned. article-nov-4572 status code = 200: The request succeeded, and the resource is returned. article-nov-1118 status code = 200: The request succeeded, and the resource is returned. article-nov-4319 status code = 200: The request succeeded, and the resource is returned. article-nov-0662 status code = 200: The request succeeded, and the resource is returned. article-nov-0319 status code = 200: 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succeeded, and the resource is returned. article-nov-0761 status code = 200: The request succeeded, and the resource is returned. article-nov-4428 status code = 200: The request succeeded, and the resource is returned. article-nov-4174 status code = 200: The request succeeded, and the resource is returned. article-nov-2399 status code = 200: The request succeeded, and the resource is returned. article-nov-2282 status code = 200: The request succeeded, and the resource is returned. article-nov-1288 status code = 200: The request succeeded, and the resource is returned. article-nov-3650 status code = 200: The request succeeded, and the resource is returned. article-nov-0435 status code = 200: The request succeeded, and the resource is returned. article-nov-1149 status code = 200: The request succeeded, and the resource is returned. article-nov-0147 status code = 200: The request succeeded, and the resource is returned. article-nov-1264 status code = 200: The request succeeded, and the resource is returned. article-nov-2018 status code = 200: The request succeeded, and the resource is returned. article-nov-2115 status code = 200: The request succeeded, and the resource is returned. article-nov-4659 status code = 200: The request succeeded, and the resource is returned. article-nov-2397 status code = 200: The request succeeded, and the resource is returned. article-nov-1341 status code = 200: The request succeeded, and the resource is returned. article-nov-3574 status code = 200: The request succeeded, and the resource is returned. article-nov-2226 status code = 200: The request succeeded, and the resource is returned. article-nov-0560 status code = 200: The request succeeded, and the resource is returned. article-nov-4374 status code = 200: The request succeeded, and the resource is returned. article-nov-0575 status code = 200: The request succeeded, and the resource is returned. article-nov-1388 status code = 200: The request succeeded, and the resource is returned. article-nov-3587 status code = 200: The request succeeded, and the resource is returned. article-nov-0584 status code = 200: The request succeeded, and the resource is returned. article-nov-4426 status code = 200: The request succeeded, and the resource is returned. article-nov-2469 status code = 200: The request succeeded, and the resource is returned. article-nov-1794 status code = 200: The request succeeded, and the resource is returned. article-nov-1348 status code = 200: The request succeeded, and the resource is returned. article-dec-0244 status code = 200: The request succeeded, and the resource is returned. article-dec-0895 status code = 200: The request succeeded, and the resource is returned. article-dec-4490 status code = 200: The request succeeded, and the resource is returned. article-dec-0891 status code = 200: The request succeeded, and the resource is returned. article-dec-0672 status code = 200: The request succeeded, and the resource is returned. article-dec-3487 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resource is returned. article-dec-0180 status code = 200: The request succeeded, and the resource is returned. article-dec-3784 status code = 200: The request succeeded, and the resource is returned. article-dec-3423 status code = 200: The request succeeded, and the resource is returned. article-dec-4596 status code = 200: The request succeeded, and the resource is returned. article-dec-1585 status code = 200: The request succeeded, and the resource is returned. article-dec-2351 status code = 200: The request succeeded, and the resource is returned. article-dec-2624 status code = 200: The request succeeded, and the resource is returned. article-dec-1865 status code = 200: The request succeeded, and the resource is returned. article-dec-0232 status code = 200: The request succeeded, and the resource is returned. article-dec-4377 status code = 200: The request succeeded, and the resource is returned. article-dec-4926 status code = 200: The request succeeded, and the resource is returned. 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List articles_body contains a list of strings - articles. Each article unnecessaraly contains:
- Title of the article two times, instead of just once, and
- Each article ends with: 'Return to article search results' OR 'Comments are closed for this article.' Home | Terms & Conditions | Privacy Policy | Cookie Information © 2017 — Original rights holders
These sentences at the end don't have much to do with the article and hence the article category. We need to exclude them in orther for them not to influence similarity between documents/articles. Also, there is no need to have the Title writen twice, as that would affect our term vector/ term matrix (as we would count the appearance of the words in title twice)
Hence, we are going to use list aricles_b_p_no_end.
In [8]:
print('Number of articles in articles_body',len(articles_body))
print('Number of articles in aricles_b_p',len(aricles_b_p))
print('Number of articles in aricles_b_p_no_end', len(aricles_b_p_no_end))
#print(articles_body[0])
#print(aricles_b_p[0]) #unnecessary
#Let's check how the 1st article looks like
print('\n',aricles_b_p_no_end[0])
#print(aricles_b_p_no_end[0:5]) #prints the list containing first five articles
Number of articles in articles_body 1408 Number of articles in aricles_b_p 1408 Number of articles in aricles_b_p_no_end 1408 21st-Century Sports: How Digital Technology Is Changing the Face Of The Sporting Industry The sporting industry has come a long way since the ‘60s. It has carved out for itself a niche with its roots so deep that I cannot fathom the sports industry showing any sign of decline any time soon - or later. The reason can be found in this seemingly subtle difference - other industries have customers; the sporting industry has fans. Vivek Ranadivé, leader of the ownership group of the NBA’s Sacramento Kings, explained it beautifully, “Fans will paint their face purple, fans will evangelize. ... Every other CEO in every business is dying to be in our position — they’re dying to have fans.“ While fan passion alone could almost certainly keep the industry going, leagues and sporting franchises have decided not to rest on their laurels. The last few years have seen the steady introduction of technology into the world of sports - amplifying fans’ appreciation of games, enhancing athletes’ public profiles and informing their training methods, even influencing how contests are waged. Also, digital technology in particular has helped to create an alternative source of revenue, besides the games themselves - corporate sponsorship. They achieved this by capitalizing on the ardor of their customer base - sorry, fan base.
We will store these articles in a single file and formatted so that one article appears on each line.
In [9]:
#Saving the body of each article as plain text.
for i in range(len(aricles_b_p_no_end)):
fout = open("news-articles.txt","a")
fout.write(aricles_b_p_no_end[i])
fout.write("\n") #need to explicitly move to next line, so that each article appears on each line
fout.close()
Saving the category labels for all articles in a separate file.
In [10]:
#Saving the category labels for all articles in a separate file.
for i in range(len(aricles_b_p_no_end)):
fout = open("labels.txt","a") #"a"- to add data to the end of an existing file
fout.write(labels[i])
fout.write("\n") #need to explicitly move to next line
fout.close()
Part 2. Text Classification¶
The goal here is to analyse the corpus of documents from Part 1 in a text classification context. Tasks to be completed:
- From the files created in Part 1, load the set of raw documents into your notebook. Ensure that each document has a class label, based on the original category label that you identified.
- From the raw documents, create a document-term matrix, using appropriate text pre-processing and term weighting steps.
- Build two multi-class classification models using two different classifiers of your choice.
- Compare the predictions of the two classification models using an appropriate evaluation strategy. Report and discuss the evaluation results in your notebook.
We will look at a range of text mining techniques, available as part of Scikit-learn.
As our sample corpus of text, we will read a collection of news articles. These articles have been stored in a single file and formatted so that one article appears on each line.
In [291]:
#Loading the set of raw documents into the notebook as raw_documents list
fin = open("news-articles.txt","r") # "r" - read
raw_documents = fin.readlines() #read all lines from a file to a list
fin.close()
print("Read %d raw text documents/articles" % len(raw_documents))
Read 1408 raw text documents/articles
In [292]:
#Loading class labels into the notebook as class_labels list
#Each document has a class label, based on the original category label that was identified.
fin = open("labels.txt","r")
class_labels = fin.readlines()
fin.close()
print("Read %d class labels" % len(class_labels))
#print(class_labels)
Read 1408 class labels
From the raw documents, create a document-term matrix, using appropriate text pre-processing and term weighting steps.¶
Note: Several following code/markdown cells serve to showcase pre-rocessing techniques needed for creating document-term matrix.¶
If not relevant, skip to "Creating Document Term Matrix & using appropriate Text Pre-processing" pharagraph.¶
Tokenizing Text¶
Raw text documents are textual, not numeric. The first step in analysing unstructured documents is to split the raw text into individual tokens, each corresponding to a single term (word). As an example:
In [293]:
doc1 = raw_documents[0] #1st document/article from raw_documents
# print a snippet
print(doc1[0:300]) #print first 300 characters from first document
21st-Century Sports: How Digital Technology Is Changing the Face Of The Sporting Industry The sporting industry has come a long way since the ‘60s. It has carved out for itself a niche with its roots so deep that I cannot fathom the sports industry showing any sign of decline any time soon - or lat
We will use the built-in scikit-learn tokenizer to split this document into tokens. Note that we will perform case conversion first to convert the entire text to lowercase.
In [294]:
tokenize = CountVectorizer().build_tokenizer()
# convert to lowercase, then tokenize
tokens1 = tokenize(doc1.lower()) #doc1.lower()-case conversion; tokenize(doc1.lower())-splitting into tokens
print(tokens1)
['21st', 'century', 'sports', 'how', 'digital', 'technology', 'is', 'changing', 'the', 'face', 'of', 'the', 'sporting', 'industry', 'the', 'sporting', 'industry', 'has', 'come', 'long', 'way', 'since', 'the', '60s', 'it', 'has', 'carved', 'out', 'for', 'itself', 'niche', 'with', 'its', 'roots', 'so', 'deep', 'that', 'cannot', 'fathom', 'the', 'sports', 'industry', 'showing', 'any', 'sign', 'of', 'decline', 'any', 'time', 'soon', 'or', 'later', 'the', 'reason', 'can', 'be', 'found', 'in', 'this', 'seemingly', 'subtle', 'difference', 'other', 'industries', 'have', 'customers', 'the', 'sporting', 'industry', 'has', 'fans', 'vivek', 'ranadivé', 'leader', 'of', 'the', 'ownership', 'group', 'of', 'the', 'nba', 'sacramento', 'kings', 'explained', 'it', 'beautifully', 'fans', 'will', 'paint', 'their', 'face', 'purple', 'fans', 'will', 'evangelize', 'every', 'other', 'ceo', 'in', 'every', 'business', 'is', 'dying', 'to', 'be', 'in', 'our', 'position', 'they', 're', 'dying', 'to', 'have', 'fans', 'while', 'fan', 'passion', 'alone', 'could', 'almost', 'certainly', 'keep', 'the', 'industry', 'going', 'leagues', 'and', 'sporting', 'franchises', 'have', 'decided', 'not', 'to', 'rest', 'on', 'their', 'laurels', 'the', 'last', 'few', 'years', 'have', 'seen', 'the', 'steady', 'introduction', 'of', 'technology', 'into', 'the', 'world', 'of', 'sports', 'amplifying', 'fans', 'appreciation', 'of', 'games', 'enhancing', 'athletes', 'public', 'profiles', 'and', 'informing', 'their', 'training', 'methods', 'even', 'influencing', 'how', 'contests', 'are', 'waged', 'also', 'digital', 'technology', 'in', 'particular', 'has', 'helped', 'to', 'create', 'an', 'alternative', 'source', 'of', 'revenue', 'besides', 'the', 'games', 'themselves', 'corporate', 'sponsorship', 'they', 'achieved', 'this', 'by', 'capitalizing', 'on', 'the', 'ardor', 'of', 'their', 'customer', 'base', 'sorry', 'fan', 'base']
We immediately see that many of the words here are not useful (e.g. "while", "the" etc.). Scikit-learn provides a list of such stop words:
In [295]:
from sklearn.feature_extraction import text
#List of English stop words
stopwords = text.ENGLISH_STOP_WORDS
print(stopwords)
frozenset({'fifteen', 'these', 'a', 'own', 'co', 'had', 'however', 'only', 'with', 'un', 'too', 'move', 'several', 'others', 'still', 'hasnt', 'whereupon', 'thereby', 'whole', 'see', 'noone', 'per', 'can', 'has', 'whether', 'because', 'last', 'already', 'once', 'beside', 'those', 'almost', 'go', 'amongst', 'beyond', 'part', 'ten', 'would', 'perhaps', 'mill', 'whither', 'fire', 'and', 'formerly', 'again', 'one', 'what', 'front', 'becoming', 'below', 'everyone', 'everything', 'for', 'latter', 'yet', 'anywhere', 'nobody', 'she', 'themselves', 'couldnt', 'well', 'none', 'at', 'wherein', 'himself', 'mine', 'few', 'whoever', 'amount', 'cannot', 'indeed', 'someone', 'some', 'when', 'thin', 'but', 'will', 'nor', 'though', 'next', 'to', 'which', 'became', 'namely', 'keep', 'although', 'meanwhile', 'otherwise', 'more', 'anything', 'least', 'until', 'found', 'by', 'many', 'much', 'herein', 'afterwards', 'anyone', 're', 'into', 'across', 'ever', 'due', 'her', 'he', 'them', 'enough', 'so', 'either', 'everywhere', 'its', 'anyway', 'could', 'former', 'it', 'about', 'get', 'this', 'third', 'etc', 'whatever', 'often', 'him', 'somehow', 'were', 'fifty', 'hence', 'was', 'we', 'where', 'first', 'behind', 'whereby', 'whenever', 'side', 'done', 'in', 'cry', 'ourselves', 'must', 'put', 'an', 'that', 'describe', 'are', 'if', 'whose', 'less', 'three', 'full', 'thus', 'except', 'thence', 'con', 'the', 'yourself', 'any', 'seems', 'or', 'seeming', 'de', 'cant', 'serious', 'all', 'most', 'such', 'there', 'therefore', 'us', 'hereafter', 'twelve', 'myself', 'within', 'being', 'besides', 'bottom', 'same', 'seemed', 'detail', 'latterly', 'over', 'anyhow', 'becomes', 'do', 'give', 'herself', 'am', 'might', 'on', 'even', 'ours', 'whereafter', 'forty', 'else', 'around', 'between', 'before', 'hereupon', 'eight', 'thereafter', 'they', 'thru', 'empty', 'than', 'two', 'moreover', 'therein', 'against', 'whence', 'down', 'after', 'neither', 'under', 'four', 'here', 'somewhere', 'i', 'whereas', 'five', 'is', 'who', 'sometime', 'yourselves', 'since', 'toward', 'without', 'six', 'rather', 'throughout', 'each', 'nevertheless', 'sincere', 'both', 'yours', 'off', 'system', 'inc', 'sixty', 'nowhere', 'may', 'towards', 'always', 'thick', 'every', 'wherever', 'as', 'hundred', 'from', 'been', 'interest', 'me', 'never', 'fill', 'nothing', 'of', 'please', 'show', 'call', 'how', 'then', 'other', 'together', 'sometimes', 'elsewhere', 'itself', 'onto', 'also', 'find', 'hers', 'mostly', 'out', 'twenty', 'above', 'whom', 'very', 'be', 'ie', 'eg', 'his', 'name', 'now', 'amoungst', 'during', 'should', 'their', 'eleven', 'why', 'seem', 'not', 'our', 'bill', 'you', 'among', 'nine', 'have', 'no', 'back', 'become', 'hereby', 'another', 'thereupon', 'take', 'while', 'further', 'up', 'something', 'your', 'via', 'top', 'ltd', 'my', 'along', 'upon', 'made', 'beforehand', 'through', 'alone'})
We can filter out these stopwords from our document:
In [296]:
filtered_tokens1 = []
for token in tokens1: #tokens1 - list of lowercase terms/words/tokens from document1/article1
if not token in stopwords: #stopwords - list of english stopwords
filtered_tokens1.append(token)
print(filtered_tokens1) #filtered_tokens1 - list of lowercase tokens/terms/words without stopwords
['21st', 'century', 'sports', 'digital', 'technology', 'changing', 'face', 'sporting', 'industry', 'sporting', 'industry', 'come', 'long', 'way', '60s', 'carved', 'niche', 'roots', 'deep', 'fathom', 'sports', 'industry', 'showing', 'sign', 'decline', 'time', 'soon', 'later', 'reason', 'seemingly', 'subtle', 'difference', 'industries', 'customers', 'sporting', 'industry', 'fans', 'vivek', 'ranadivé', 'leader', 'ownership', 'group', 'nba', 'sacramento', 'kings', 'explained', 'beautifully', 'fans', 'paint', 'face', 'purple', 'fans', 'evangelize', 'ceo', 'business', 'dying', 'position', 'dying', 'fans', 'fan', 'passion', 'certainly', 'industry', 'going', 'leagues', 'sporting', 'franchises', 'decided', 'rest', 'laurels', 'years', 'seen', 'steady', 'introduction', 'technology', 'world', 'sports', 'amplifying', 'fans', 'appreciation', 'games', 'enhancing', 'athletes', 'public', 'profiles', 'informing', 'training', 'methods', 'influencing', 'contests', 'waged', 'digital', 'technology', 'particular', 'helped', 'create', 'alternative', 'source', 'revenue', 'games', 'corporate', 'sponsorship', 'achieved', 'capitalizing', 'ardor', 'customer', 'base', 'sorry', 'fan', 'base']
We will repeat this process for all documents:
In [297]:
all_filtered_tokens = []
for doc in raw_documents: #raw_documents - list of documents/articles (lines in a file)
# tokenize the next document
tokens = tokenize(doc.lower()) #tokens - list of lowercase tokens from document doc
# remove the stopwords
filtered_tokens = []
for token in tokens:
if not token in stopwords:
filtered_tokens.append(token) #list of lowercase tokens without stpowords for that document
# add to the overall list
all_filtered_tokens.append( filtered_tokens ) #all_filtered_tokens- list of lists of lowercase tokens without
#stopwords for each document in raw_documents
print("Created %d filtered token lists" % len(all_filtered_tokens) )
print('Number of tokens in 1st raw_document/article:', len(all_filtered_tokens[0]))
#all_filtered_tokens[0:3]
Created 1408 filtered token lists Number of tokens in 1st raw_document/article: 110
Counting Tokens¶
A simple type of analysis that we might do is to count the number of times specific terms (words) appear in our corpus. We could do this by creating a dictionary of term frequency counts:
In [298]:
#counts - dictionary of term frequancy counts, where:
# --> key is token/word/term, and
# --> value is the count (frequency of the token)
counts = {}
# process filtered tokens for each document
for doc_tokens in all_filtered_tokens:
for token in doc_tokens:
# increment if existing
if token in counts:
counts[token] += 1
# set up to 1 if it is a new term
else:
counts[token] = 1
print("Found %d unique terms in this corpus" % len(counts)) #22601 unique terms found
#accross doc_tokens in all_filtered_tokens
#(unique lowercase terms without stopwords)
Found 22601 unique terms in this corpus
We would like to find the terms in the dictionary with the highest counts. Python provides a convenient way of doing this.
The below creates a list of tuple pairs, where the first value is the key (i.e. the term/token) and the second value is the value (i.e the count). Let's display the top 20 terms.
In [299]:
sorted_counts = sorted(counts.items(), key=operator.itemgetter(1), reverse=True) #list of tuple pairs
In [300]:
for i in range(20):
term = sorted_counts[i][0]
count = sorted_counts[i][1]
print( "%s (count=%d)" % ( term, count ) )
said (count=4119) year (count=1557) new (count=1215) people (count=1203) mr (count=1092) world (count=960) time (count=933) game (count=881) news (count=767) online (count=728) just (count=683) market (count=644) like (count=618) games (count=608) company (count=601) players (count=599) years (count=598) make (count=597) technology (count=576) firm (count=547)
In the bag-of-words model, each document is represented by a vector in an m-dimensional coordinate space, where m is number of unique terms across all documents. This set of terms is called the corpus vocabulary. Note that the positioning (context) of terms within the original document is lost in this model.
Since each document can be represented as a term vector, we can stack these vectors to create a full document-term matrix. We can easily create this matrix from a list of document strings using Scikit-learn:
Document Term Matrix - stacked term vectors¶
In [301]:
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(raw_documents) #X - document-term matrix (each row represents document as a term vector)
print(X.shape) #(1408, 22895), where 1408 is the number of documents and
#22895 is the number of unique terms across all documents
#(stop-words are not filtered here, hence 22895 unique terms instead of 22601 like in above case )
#Note. Scikit-learn does case conversion and minimum term length by default
#print(X[0])
(1408, 22895)
This process also builds a vocabulary for the corpus, both in the form of a list and in the form of a dictionary:
In [302]:
#Vocabulary for the corpus in the form of a list (list of terms) --> how many terms in the vocabulary?
terms = vectorizer.get_feature_names()
#Vocabulary for the Corpus in the Form of a dictionary
#(dictionary which maps each unique term to a corresponding column in the matrix)-->Which column corresponds to a term?
vocab = vectorizer.vocabulary_
print("Vocabulary has %d distinct terms" % len(terms))
Vocabulary has 22895 distinct terms
Display some sample terms:
In [303]:
print(terms[10000:10050])
['helpful', 'helping', 'helpless', 'helps', 'helsinki', 'hemin', 'hemington', 'hemisphere', 'hence', 'henchoz', 'henderson', 'hendrix', 'henin', 'henk', 'henman', 'hennigar', 'henri', 'henric', 'henrik', 'henry', 'hensel', 'henson', 'heptathlon', 'her', 'herald', 'heralded', 'heralds', 'herbert', 'hercus', 'herded', 'here', 'heretic', 'heritage', 'hermann', 'herne', 'hernych', 'hero', 'heroes', 'heroic', 'heroics', 'heronry', 'herren', 'herself', 'hertfordshire', 'hertrich', 'hertzfeld', 'herve', 'hesaid', 'heseltine', 'hesitant']
Since each column in the document-term matrix correspond to a term, we can look up the column associated with each term using the dictionary:
In [304]:
# what column is the term 'year' on?
print('year column:', vocab["year"])
# what column is the term 'world' on?
print('world column:', vocab["world"])
# what column is the term 'games' on?
print('games column:', vocab["games"])
# what column is the term 'technology' on?
print('technology column:', vocab["technology"])
# what column is the term 'players' on?
print('players column:', vocab["players"])
year column: 22776 world column: 22628 games column: 9024 technology column: 20420 players column: 15620
We can use the same Scikit-learn functionality to create a document-term matrix with N-grams. We specify an extra parameter ngram_range which specifies the shortest and longest token sequences to include. Length 1 is just a single token.
For instance, transform our input documents into a matrix, extracting single tokens and bigrams:
In [305]:
vectorizer = CountVectorizer(ngram_range = (1,2)) #(1,2) - single tokens and bigrams
X = vectorizer.fit_transform(raw_documents) #X - document-term matrix where each row represents document as a term vector
#(single tokens & birams are columns)
Note the vocabulary is much larger now:
In [306]:
terms = vectorizer.get_feature_names()
vocab = vectorizer.vocabulary_
print("Vocabulary has %d distinct terms" % len(terms)) #still includes English stop-words
Vocabulary has 253007 distinct terms
Display some sample terms. Note that we see a mix of single tokens and bigrams (i.e. phrases of length 2):
In [307]:
print(terms[250000:250030])
['worries that', 'worries total', 'worries wholesale', 'worry', 'worry about', 'worry bit', 'worry centres', 'worry is', 'worry me', 'worry over', 'worry that', 'worry there', 'worry we', 'worrying', 'worrying about', 'worrying for', 'worrying if', 'worrying implications', 'worrying precedent', 'worrying signs', 'worrying telecoms', 'worrying that', 'worrying thing', 'worrying tone', 'worrying within', 'worryingly', 'worryingly easy', 'worryingly said', 'worse', 'worse 90']
Text Preprocessing¶
A range of steps can be used to process text input files to reduce the number of terms used to represent the text and to improve the resulting bag-of-words model. These include:
- Minimum term length: Exclude terms of length < 2. Scikit-learn does this by default.
- Case conversion: Converting all terms to lowercase. Scikit-learn does this by default.
- Stop-word filtering: Remove terms that appear on a pre-defined "blacklist" of terms that are highly frequent and do- not convey useful information.
- Low frequency filtering: Remove terms that appear in very few documents.
- Stemming: Reduce words to their stems (or base forms).
- Lemmatization: reduces a term to its canonical form (more advanced from stemming)
Scikit-learn allows us to perform one or more of these steps by adapting the CountVectorizer.
We can use the built-in list of stop-words for a given language by just specifying the name of the language (lower-case):
In [308]:
vectorizer = CountVectorizer(stop_words="english")
X = vectorizer.fit_transform(raw_documents)
print("Number of terms in model is %d" % len(vectorizer.vocabulary_) )
# Are standard stopwords gone? Let's check if "the" is in vocabulary
"the" in vectorizer.vocabulary_ #we get "false", hence built-in english stop-words were filtered out
Number of terms in model is 22601
Out[308]:
False
Or we could use our own custom stop-word list, which might be more appropriate for specific applications:
In [309]:
custom_stop_words = [ "and", "the", "while" , "or"]
vectorizer = CountVectorizer(stop_words=custom_stop_words)
X = vectorizer.fit_transform(raw_documents)
print("Number of terms in model is %d" % len(vectorizer.vocabulary_) )
# Are custom stopwords gone?
"while" in vectorizer.vocabulary_ #custom stop-words are gone
Number of terms in model is 22891
Out[309]:
False
We can remove low frequency terms that appear in fewer than a specified number of documents:
In [310]:
# how many terms did we have with when we don't filter out stop words?
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(raw_documents)
print("Number of terms in model is %d" % len(vectorizer.vocabulary_) )
Number of terms in model is 22895
In [311]:
# build another matrix, but filter terms appearing in less than 5 documents
vectorizer = CountVectorizer(min_df = 5)
X = vectorizer.fit_transform(raw_documents)
print("Number of terms in model is %d" % len(vectorizer.vocabulary_) )
Number of terms in model is 7078
To stem tokens to their base form, we need to use functionality from another third party library.
We can test out the standard English stemming algorithm (called the Porter Stemmer):
In [312]:
# import the standard English stemming algorithm
from nltk.stem.porter import PorterStemmer
words = [sorted_counts[i][0] for i in range(20)] #list of top20 terms in the dictionary with the highest counts
print(words)
# trying stemming each sample word
stemmer = PorterStemmer()
for w in words:
print( stemmer.stem(w) )
['said', 'year', 'new', 'people', 'mr', 'world', 'time', 'game', 'news', 'online', 'just', 'market', 'like', 'games', 'company', 'players', 'years', 'make', 'technology', 'firm'] said year new peopl mr world time game news onlin just market like game compani player year make technolog firm
To use NLTK stemming with Scikit-learn, we need to create a custom tokenisation function:
In [313]:
import nltk
# define the function
def stem_tokenizer(text):
# use the standard scikit-learn tokenizer first
standard_tokenizer = CountVectorizer().build_tokenizer()
tokens = standard_tokenizer(text)
# then use NLTK to perform stemming on each token
stemmer = PorterStemmer()
stems = []
for token in tokens:
stems.append( stemmer.stem(token) )
return stems
Now we can use our custom tokenizer with the standard CountVectorizer approach:
In [314]:
vectorizer = CountVectorizer(tokenizer=stem_tokenizer)
X = vectorizer.fit_transform(raw_documents)
# display some sample terms
terms = vectorizer.get_feature_names()
print(terms[1400:1420])
['affect', 'afficiando', 'affili', 'affirm', 'afflict', 'affluent', 'afford', 'afghanistan', 'afield', 'afoot', 'afp', 'afraid', 'africa', 'african', 'after', 'aftermath', 'afternoon', 'afterward', 'afteward', 'ag']
We can perform lemmatisation in the same way, using NLTK with Sckit-learn (WordNetLemmatizer()):
In [315]:
# define the function
def lemma_tokenizer(text):
# use the standard scikit-learn tokenizer first
standard_tokenizer = CountVectorizer().build_tokenizer()
tokens = standard_tokenizer(text)
# then use NLTK to perform lemmatisation on each token
lemmatizer = nltk.stem.WordNetLemmatizer()
lemma_tokens = []
for token in tokens:
lemma_tokens.append( lemmatizer.lemmatize(token) )
return lemma_tokens
Again we can use our custom tokenizer with the standard CountVectorizer approach. The output terms from lemmatization are somewhat easier to intrepret than those produced by stemming:
In [316]:
#[Link](https://stackoverflow.com/questions/13965823/resource-corpora-wordnet-not-found-on-heroku)
#nltk.download("wordnet", "whatever_the_absolute_path_to_myapp_is/nltk_data/")
nltk.download("wordnet", "/Users/aidasehic/Desktop/PYTHON/nltk_data/")
[nltk_data] Downloading package wordnet to [nltk_data] /Users/aidasehic/Desktop/PYTHON/nltk_data/... [nltk_data] Package wordnet is already up-to-date!
Out[316]:
True
In [317]:
nltk.data.path.append('/Users/aidasehic/Desktop/PYTHON/nltk_data')
In [318]:
vectorizer = CountVectorizer(tokenizer=lemma_tokenizer) #function lemma_tokenizer defined above
X = vectorizer.fit_transform(raw_documents)
# display some sample terms
print(list(vectorizer.vocabulary_.keys())[0:35])
['21st', 'century', 'sport', 'how', 'digital', 'technology', 'is', 'changing', 'the', 'face', 'of', 'sporting', 'industry', 'ha', 'come', 'long', 'way', 'since', '60', 'it', 'carved', 'out', 'for', 'itself', 'niche', 'with', 'root', 'so', 'deep', 'that', 'cannot', 'fathom', 'showing', 'any', 'sign']
Creating Document Term Matrix & using appropriate Text Pre-processing¶
In [342]:
Image(filename='text_processing_steps.png')
Out[342]:
Let's put all of these steps together: stop words, low frequency terms, lemmatization.¶
Note: Scikit-learn does Minimum term length and Case conversion by default.
In [319]:
#does case conversion and min. term lenght by default
vectorizer_preprocessing = CountVectorizer(stop_words="english",min_df = 3,tokenizer=lemma_tokenizer)
X_preproc = vectorizer_preprocessing.fit_transform(raw_documents)
print(X_preproc.shape) # By default (1408, 22895), with all the steps (1408, 8750)
(1408, 8750)
In [320]:
print(list(vectorizer_preprocessing.vocabulary_.keys())[8000:8035])
['adelaide', 'hardcourt', 'ignacio', 'chela', 'melzer', 'kiefer', 'joachim', 'enqvist', 'pure', 'palmer', 'plea', 'retrieve', 'deleted', 'quinlan', 'purely', 'costello', 'duffy', 'citizenship', 'incumbent', 'ill', 'kenya', 'ak', 'isaiah', 'snack', 'obesity', 'wisely', 'quota', 'rand', 'organization', 'hat', 'taiwan', 'mauritius', 'mountainous', 'bus', 'donor']
Let's use N-Grams in combination with all of those steps together (stop words, low frequency terms, lemmatization).¶
BIGRAMS - building terms from every pair of adjacent tokens (N-GRAMS - N adjacent tokens) with a goal of solving the problem with losing the order of words in a sentence (that bag of words representation has). Note: Scikit-learn does Minimum term length and Case conversion by default.
In [321]:
vectorizer_ngrams = CountVectorizer(stop_words="english",min_df = 3,tokenizer=lemma_tokenizer, ngram_range=(1,3)) #does case conversion and min. term lenght by default
X_ngrams = vectorizer_ngrams.fit_transform(raw_documents)
print(X_ngrams.shape) # By default (1408, 22895), with all the steps (1408, 8750),
# and then by applying bigrams vocabulary gets much larger (1408, 23035) --> from 8750 to 23035
(1408, 23035)
In [322]:
#Display some sample terms. Note that we see a mix of single tokens and bigrams and threegrams(eg 'mobile phone music')
print(list(vectorizer_ngrams.vocabulary_.keys())[18000:18035])
['u service', 'people transfer', 'report mobile', 'tv signal', 'people watch', 'hour day', 'standard mobile', '2004 european', 'video broadcasting', 'service need', 'need addressed', 'need able', 'tv service', 'good quality', 'price according', 'small screen', 'screen said', 'like europe', 'control tv', 'offering web', 'text multimedia', 'music download service', 'battery life said', 'mobile phone music', 'apple itunes napster', 'let people watch', 'people watch tv', 'digital video broadcasting', 'people control tv', 'marca', 'burnley', 'liverpool striker', 'said despite', 'southampton league', 'lot change']
Term Weighting¶
As well as including/excluding terms, we can also modify or weight the frequency values themselves. We can improve the usefulness of the document-term matrix by giving more weight to the more "important" terms.
The most common normalisation is term frequency–inverse document frequency (TF-IDF). In Scikit-learn, we can generate TF-IDF weighted document-term matrix by using TfidfVectorizer() in place of CountVectorizer().
In [323]:
Image(filename='term_weighting.png')
Out[323]:
In [324]:
from sklearn.feature_extraction.text import TfidfVectorizer
# we can pass in the same preprocessing parameters
vectorizer_term_weighting = TfidfVectorizer(stop_words="english",min_df = 5,tokenizer=lemma_tokenizer) #we are using TfidfVectorizer in place of CountVectorizer
X_term_weighting = vectorizer_term_weighting.fit_transform(raw_documents)
# display some sample weighted values
print(X_term_weighting.shape) #(1408, 6101)
print((X_term_weighting[0]).shape) #(1, 6101)
print(X_term_weighting[0]) #6101 terms in first raw (first document), how many times that term appears and its weithted sum
(1408, 6101) (1, 6101) (0, 93) 0.10009099976626194 (0, 1026) 0.09160342032326424 (0, 5148) 0.17445620312826257 (0, 1683) 0.11925410743541424 (0, 5452) 0.1507477477570906 (0, 1047) 0.08585261081116488 (0, 2134) 0.1068101503253814 (0, 5149) 0.35790572254579966 (0, 2877) 0.3243104496271507 (0, 2567) 0.08671804858323616 (0, 1197) 0.04438106399392263 (0, 3336) 0.0486462301884699 (0, 5938) 0.03995929838497884 (0, 173) 0.0769592043968702 (0, 3729) 0.10412064656284394 (0, 4715) 0.11663787280242364 (0, 1557) 0.08640830204688155 (0, 4978) 0.0769592043968702 (0, 4986) 0.06773443932390552 (0, 1552) 0.07764073962772206 (0, 5549) 0.03197428788594128 (0, 5086) 0.06834665185841297 (0, 3199) 0.06165824190545424 (0, 4420) 0.06986142901071984 (0, 4863) 0.1138541365505874 : : (0, 4605) 0.07227281103526628 (0, 6076) 0.02569741609583775 (0, 4864) 0.05593350971228571 (0, 5199) 0.10267519210688146 (0, 2975) 0.10412064656284394 (0, 6039) 0.036268993333807314 (0, 2408) 0.07873154361861318 (0, 1963) 0.10009099976626194 (0, 563) 0.07799140191219758 (0, 4302) 0.06299652370132802 (0, 4242) 0.0869816371923765 (0, 5620) 0.07078771103051204 (0, 3542) 0.09015796586730176 (0, 1346) 0.0869816371923765 (0, 3937) 0.07475805222298283 (0, 2661) 0.06694916168090415 (0, 1437) 0.06834665185841297 (0, 413) 0.09015796586730176 (0, 5097) 0.07417546814503394 (0, 4636) 0.06814026629520249 (0, 1400) 0.07566875982160999 (0, 5146) 0.10412064656284394 (0, 273) 0.0869816371923765 (0, 669) 0.1728166040937631 (0, 5090) 0.10931576483922663
In [100]:
print(X_preproc[0].shape)
#print(X_term_weighting[0])
(1, 8750)
In [325]:
# we can pass in the same preprocessing parameters as above plus n-gram
vectorizer_term_weighting_ngram = TfidfVectorizer(stop_words="english",min_df = 5,tokenizer=lemma_tokenizer, ngram_range=(1,3)) #we are using TfidfVectorizer in place of CountVectorizer
X_term_weighting_ngram = vectorizer_term_weighting_ngram.fit_transform(raw_documents)
# display some sample weighted values
print(X_term_weighting_ngram.shape) #(1408, 10255)
print((X_term_weighting_ngram[0]).shape) #(1, 10255)
print(X_term_weighting_ngram[0]) #10255 terms in first raw (first document), how many times that term appears and its weithted sum
(1408, 10255) (1, 10255) (0, 183) 0.09380673860827529 (0, 1536) 0.08585205588869442 (0, 8468) 0.16350288720925907 (0, 2548) 0.11176668142271742 (0, 8886) 0.14128297851613855 (0, 1576) 0.08046231369461827 (0, 3225) 0.10010402409489128 (0, 8474) 0.3354344410552749 (0, 4606) 0.3039484633698402 (0, 3998) 0.08127341454340592 (0, 1798) 0.04159457772384404 (0, 5336) 0.04559195342462719 (0, 9862) 0.03745043477758618 (0, 325) 0.07212728404368948 (0, 6145) 0.09758338210882365 (0, 7628) 0.10931470832895289 (0, 2364) 0.08098311559106765 (0, 8221) 0.07212728404368948 (0, 8229) 0.06348170024551435 (0, 2358) 0.07276602876001911 (0, 9054) 0.029966766971122386 (0, 8378) 0.06405547472405251 (0, 5112) 0.05778699977997795 (0, 7249) 0.06547514587039004 (0, 8019) 0.10670575028537059 : : (0, 2987) 0.09380673860827529 (0, 867) 0.07309467454571807 (0, 7069) 0.05904125691501418 (0, 6995) 0.08152045361600475 (0, 9189) 0.06634327083178623 (0, 5708) 0.08449735498014835 (0, 2021) 0.08152045361600475 (0, 6464) 0.07006433225886287 (0, 4287) 0.06274572663386117 (0, 2148) 0.06405547472405251 (0, 634) 0.08449735498014835 (0, 8394) 0.06951832600010718 (0, 7524) 0.06386204717686227 (0, 2082) 0.07091786064655498 (0, 8466) 0.09758338210882365 (0, 460) 0.08152045361600475 (0, 1018) 0.1619662311821353 (0, 8384) 0.10245232240645041 (0, 184) 0.10066912453077775 (0, 2561) 0.21862941665790578 (0, 4609) 0.17170411177738884 (0, 4021) 0.08207541238814604 (0, 1802) 0.10931470832895289 (0, 5347) 0.08811203814273888 (0, 4056) 0.08811203814273888
We heave now created two document-term matrices that we will use as our input data with two classification models (KNN and SVM):
- X_term_weighting - document-term matrix that has filtered out english stop words, terms that appear less than 5 times, and on all terms are reduced to its canonical form (lemmatization). Also all words are lower case and more weights are given to the more "important" terms.
- X_term_weighting_ngram - same as document_term matrix above except it uses three-grams (with a goal of solving the problem with losing the order of words in a sentence (that bag of words representation has).
Before building two multi-class classification models on this data, let's check how we could measure whether two documents are similar and hence have the same class label.
Measuring Similarity¶
Cosine similarity: Most common approach for measuring similarity between two documents in a bag-of-words representation is to look at the cosine of the angle between their corresponding two term vectors. The motivation is that vectors for documents containing similar terms will point in the same direction in the m-dimensional vector space. Cosine similarity score is 1 if two documents are identical, and -1 if two documents share no terms in common
In [326]:
Image(filename='measuring_similarity.png')
Out[326]:
As an example, let's find the most similar document to the first document in our collection.
In [327]:
# First document - just display the start of it
print(raw_documents[0][0:300])
21st-Century Sports: How Digital Technology Is Changing the Face Of The Sporting Industry The sporting industry has come a long way since the ‘60s. It has carved out for itself a niche with its roots so deep that I cannot fathom the sports industry showing any sign of decline any time soon - or lat
In [328]:
from sklearn.metrics.pairwise import cosine_similarity
# Measure the cosine similarity between the first document vector and all of the others
max_cos = 0
best_row = 0
for row in range(1,X_term_weighting.shape[0]): #exclude 1st row/document for which we are trying to find the most similar document
#X.shape[0] - number of rows in X, number of documents/articles
cos = cosine_similarity( X_term_weighting[0], X_term_weighting[row] ) #cosine between two n-dim vectors (n=8750=X_term_weighting.shape[1])
# best so far?
if cos > max_cos:
max_cos = cos
best_row = row
In [329]:
print("Most similar document was row %d, with cosine similarity = %.3f" % ( best_row, max_cos ) )
Most similar document was row 1027, with cosine similarity = 0.189
In [331]:
# Best document - just display the start of it
print(raw_documents[best_row][0:300])
cosine_similarity( X_term_weighting[0], X_term_weighting[1027] )#0.18862176 (closer to one more similar they are)
Sporting rivals go to extra time The current slew of sports games offers unparalleled opportunities for fans who like to emulate on-field action without ever moving from the couch. The two giants in the field - ESPN and EA Sports - have been locked in a heavyweight battle for years. The latter is
Out[331]:
array([[0.18862176]])
In [340]:
#Cosine Similarity between the first document with all documents in the set.
#Note that the first value of the array is 1.0 because it is the Cosine Similarity between
#the first document with itself.
cosine_similarity(X_term_weighting[0:1],X_term_weighting)
#cosine_similarity(X_term_weighting[0:1],X_term_weighting)[0][1027] #0.18862175501900125
Out[340]:
array([[1. , 0.03439677, 0.04066204, ..., 0.01457144, 0.01145571,
0.05017004]])
In [341]:
(X_term_weighting * X_term_weighting.T).A
Out[341]:
array([[1. , 0.03439677, 0.04066204, ..., 0.01457144, 0.01145571,
0.05017004],
[0.03439677, 1. , 0.04593687, ..., 0.04492278, 0.04691169,
0.01780043],
[0.04066204, 0.04593687, 1. , ..., 0.01465356, 0.00814729,
0.01158035],
...,
[0.01457144, 0.04492278, 0.01465356, ..., 1. , 0.69923545,
0.01501832],
[0.01145571, 0.04691169, 0.00814729, ..., 0.69923545, 1. ,
0.02698089],
[0.05017004, 0.01780043, 0.01158035, ..., 0.01501832, 0.02698089,
1. ]])
Part 2. Text Classification¶
The goal here is to analyse the corpus of documents from Part 1 in a text classification context. Tasks to be completed:
- Build two multi-class classification models using two different classifiers of your choice.
- Compare the predictions of the two classification models using an appropriate evaluation strategy. Report and discuss the evaluation results in your notebook.
We have created document-term matrix in previous steps: X_term_weighting and X_term_weighting_ngram Subsequent modelling steps can then be applied to the document-term matrix - e.g. document classification, documenter clustering.
Text Classification¶
- Goal: To learn a model from the training set so that we can accurately predict classes for new unlabeled documents.
- Input: Training set of labelled text documents, annotated with in our case three class labels (categories).
In [343]:
Image(filename='text_classification.png')
Out[343]:
- When we have labelled documents to use as training data, we can apply many standard classifiers to a document-term matrix, using the functionality from Scikit-learn that we saw previously.
A number of general purpose classification algorithms are frequently used for classifying text documents:
- kNN: Standard nearest neighbour classifier, using an appropriate similarity measure (e.g. Cosine).
- Naive Bayes: Classification based on term frequency counts. Incorrectly assumes all terms are independent, but can still be effective in practice.
- Support Vector Machines: Often apply SVMs with a linear kernel to calculate document similarity.
I will be using kNN and SVM. The reason not to go with Naive Bayes is that it incorrectly assumes all terms are independent, even though that is not the case (Barack Obama are not independent terms).
kNN: An document is classified by a majority vote of its neighbors, with the document being assigned to the class (sport/business/technology)that is most common among its k nearest neighbors How odocument is close/far from the other document depends on the certain measure of similarity we are going to use (eg hamming distance, cosine, euclidean, etc). We saw that with Cosine_similarity the bigger the number, the more similar documents are (hence: 1-cosine_similarity)
For a start lets Partition the dataset for evaluation and Train a model¶
- As before, we will need to split training and test data for evaluations, with 2 sets of documents: train_documents, test_documents
In [345]:
#from sklearn import KNeighborsClassifier
from sklearn.model_selection import train_test_split
import numpy as np
X_train, X_test, y_train, y_test = train_test_split(X_term_weighting, class_labels, test_size=0.33, random_state=0)
knn= KNeighborsClassifier(n_neighbors=205,algorithm='brute', metric='cosine')
knn.fit(X_train, y_train)
predicted= knn.predict(X_test)
accuracy_score(y_test, predicted) * 100
Out[345]:
96.34408602150538
In [346]:
from sklearn import datasets, svm
from sklearn.model_selection import train_test_split
import numpy as np
X_train, X_test, y_train, y_test = train_test_split(X_term_weighting, class_labels, test_size=0.33, random_state=0)
svc_linear= svm.SVC(kernel='linear', C=1)
svc_linear.fit(X_train, y_train)
predicted= svc_linear.predict(X_test)
accuracy_score(y_test, predicted) * 100
Out[346]:
98.49462365591398
k-fold cross-validation¶
To compare the performance of kNN and SVM algorithms, we will use standard classifier evaluation methods - measure each classifier's mean accuracy in a k-fold cross-validation experiment.
We will use stratisfiedKFold - variation of KFold that returns stratified folds. The folds are stratified, meaning that the algorithm attempts to balance the number of instances of each class in each fold.
k-Nearest Neighbors Classifier¶
In [375]:
#Cosine distance is defined as 1.0 minus the cosine similarity.
# creating odd list of K for KNN
neighbors = list(range(1,300,2))
cvscores_not_bal = []
k_model_accuracy_not_bal=[]
kfold = StratifiedKFold(n_splits=7, shuffle=True, random_state=seed)
for k in neighbors:
fold=0
model = KNeighborsClassifier(n_neighbors=k,algorithm='brute', metric='cosine')
for train, test in kfold.split(X_term_weighting, class_labels):
fold+=1
print('FOLD',fold, 'Number of neighbors', k)
labels_train=[]
for i in range(len(train)):
labels_train.append(class_labels[train[i]])
labels_test=[]
for i in range(len(test)):
labels_test.append(class_labels[test[i]])
# Fit/Train the model
model.fit(X_term_weighting[train], labels_train)
#Evaluate the Model; Use the test dataset to evaluate the model
print('\n\n ****** Test Data ******** (Fold',fold,'):')
# Make a set of predictions for the validation data
predicted = model.predict(X_term_weighting[test])
# Print performance details
print(metrics.classification_report(labels_test, predicted))
# Print confusion matrix
print('Confusion Matrix (Fold',fold,'):')
print(metrics.confusion_matrix(labels_test, predicted))
cvscores_not_bal.append(accuracy_score(labels_test, predicted) * 100)
print("\n\n Model accuracy (for",k," neighbours): %.2f%% (+/- %.2f%%)" % (numpy.mean(cvscores_not_bal), numpy.std(cvscores_not_bal)))
k_model_accuracy_not_bal.append(numpy.mean(cvscores_not_bal))
FOLD 1 Number of neighbors 1
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.93 0.96 0.94 71
sport
1.00 0.95 0.97 76
technology
0.93 0.96 0.95 56
avg / total 0.96 0.96 0.96 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 3 72 1]
[ 2 0 54]]
FOLD 2 Number of neighbors 1
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
1.00 0.87 0.93 70
sport
0.95 0.99 0.97 75
technology
0.87 0.96 0.92 56
avg / total 0.94 0.94 0.94 201
Confusion Matrix (Fold 2 ):
[[61 2 7]
[ 0 74 1]
[ 0 2 54]]
FOLD 3 Number of neighbors 1
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.96 0.97 75
technology
0.97 1.00 0.98 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[67 2 1]
[ 2 72 1]
[ 0 0 56]]
FOLD 4 Number of neighbors 1
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.93 0.97 0.95 70
sport
0.99 0.93 0.96 75
technology
0.95 0.96 0.96 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 4 ):
[[68 1 1]
[ 3 70 2]
[ 2 0 54]]
FOLD 5 Number of neighbors 1
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
1.00 0.90 0.95 70
sport
0.99 0.97 0.98 75
technology
0.88 1.00 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 5 ):
[[63 1 6]
[ 0 73 2]
[ 0 0 56]]
FOLD 6 Number of neighbors 1
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.93 0.94 0.94 70
sport
0.96 0.97 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 2 2]
[ 1 73 1]
[ 4 1 51]]
FOLD 7 Number of neighbors 1
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.89 0.89 0.89 70
sport
0.91 0.96 0.94 75
technology
0.90 0.84 0.87 55
avg / total 0.90 0.90 0.90 200
Confusion Matrix (Fold 7 ):
[[62 4 4]
[ 2 72 1]
[ 6 3 46]]
Model accuracy (for 1 neighbours): 94.60% (+/- 2.07%)
FOLD 1 Number of neighbors 3
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.99 0.97 0.98 76
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 74 1]
[ 1 1 54]]
FOLD 2 Number of neighbors 3
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.89 0.93 70
sport
0.97 0.96 0.97 75
technology
0.87 0.98 0.92 56
avg / total 0.94 0.94 0.94 201
Confusion Matrix (Fold 2 ):
[[62 1 7]
[ 2 72 1]
[ 0 1 55]]
FOLD 3 Number of neighbors 3
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.97 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 4 Number of neighbors 3
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.99 0.95 0.97 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[68 1 1]
[ 2 71 2]
[ 1 0 55]]
FOLD 5 Number of neighbors 3
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 0.99 0.98 75
technology
0.92 0.98 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 5 ):
[[64 1 5]
[ 1 74 0]
[ 0 1 55]]
FOLD 6 Number of neighbors 3
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.94 0.96 0.95 70
sport
0.97 0.99 0.98 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 1 74 0]
[ 3 2 51]]
FOLD 7 Number of neighbors 3
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.94 0.91 0.93 70
sport
0.95 0.97 0.96 75
technology
0.93 0.93 0.93 55
avg / total 0.94 0.94 0.94 200
Confusion Matrix (Fold 7 ):
[[64 2 4]
[ 2 73 0]
[ 2 2 51]]
Model accuracy (for 3 neighbours): 95.20% (+/- 1.82%)
FOLD 1 Number of neighbors 5
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.96 0.96 71
sport
0.99 0.97 0.98 76
technology
0.93 0.96 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 1 74 1]
[ 1 1 54]]
FOLD 2 Number of neighbors 5
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.96 0.99 0.97 75
technology
0.93 0.96 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[65 1 4]
[ 1 74 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 5
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 0.97 0.97 75
technology
0.97 1.00 0.98 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 2 73 0]
[ 0 0 56]]
FOLD 4 Number of neighbors 5
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.93 0.99 0.96 70
sport
1.00 0.95 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 3 71 1]
[ 2 0 54]]
FOLD 5 Number of neighbors 5
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 0.99 0.98 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[65 2 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 5
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.94 0.94 0.94 70
sport
0.97 0.99 0.98 75
technology
0.93 0.91 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 0 4]
[ 1 74 0]
[ 3 2 51]]
FOLD 7 Number of neighbors 5
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.97 0.97 0.97 75
technology
0.96 0.96 0.96 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 1 2]
[ 2 73 0]
[ 1 1 53]]
Model accuracy (for 5 neighbours): 95.57% (+/- 1.61%)
FOLD 1 Number of neighbors 7
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.93 0.95 71
sport
0.97 0.99 0.98 76
technology
0.93 0.96 0.95 56
avg / total 0.96 0.96 0.96 203
Confusion Matrix (Fold 1 ):
[[66 1 4]
[ 1 75 0]
[ 1 1 54]]
FOLD 2 Number of neighbors 7
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.97 0.98 75
technology
0.92 0.98 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 0 4]
[ 1 73 1]
[ 0 1 55]]
FOLD 3 Number of neighbors 7
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 1.00 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 4 Number of neighbors 7
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
1.00 0.96 0.98 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 72 1]
[ 2 0 54]]
FOLD 5 Number of neighbors 7
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 1.00 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[65 2 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 7
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.93 0.90 0.91 70
sport
0.96 0.99 0.97 75
technology
0.89 0.89 0.89 56
avg / total 0.93 0.93 0.93 201
Confusion Matrix (Fold 6 ):
[[63 1 6]
[ 1 74 0]
[ 4 2 50]]
FOLD 7 Number of neighbors 7
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.97 0.97 0.97 75
technology
0.96 0.96 0.96 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 1 2]
[ 2 73 0]
[ 1 1 53]]
Model accuracy (for 7 neighbours): 95.72% (+/- 1.57%)
FOLD 1 Number of neighbors 9
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.93 0.96 71
sport
0.97 0.99 0.98 76
technology
0.92 0.96 0.94 56
avg / total 0.96 0.96 0.96 203
Confusion Matrix (Fold 1 ):
[[66 1 4]
[ 0 75 1]
[ 1 1 54]]
FOLD 2 Number of neighbors 9
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
1.00 0.97 0.99 75
technology
0.92 1.00 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 0 4]
[ 1 73 1]
[ 0 0 56]]
FOLD 3 Number of neighbors 9
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
1.00 0.93 0.96 70
sport
0.97 0.99 0.98 75
technology
0.93 1.00 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 74 1]
[ 0 0 56]]
FOLD 4 Number of neighbors 9
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.99 0.96 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[68 1 1]
[ 2 72 1]
[ 2 0 54]]
FOLD 5 Number of neighbors 9
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
0.99 0.99 0.99 75
technology
0.98 1.00 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 1 1]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 9
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.96 0.91 0.93 70
sport
0.96 0.99 0.97 75
technology
0.91 0.93 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[64 1 5]
[ 1 74 0]
[ 2 2 52]]
FOLD 7 Number of neighbors 9
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 0.96 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 9 neighbours): 95.91% (+/- 1.53%)
FOLD 1 Number of neighbors 11
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.94 0.96 71
sport
0.99 1.00 0.99 76
technology
0.93 0.96 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 1 1 54]]
FOLD 2 Number of neighbors 11
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.97 0.98 75
technology
0.92 0.98 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 0 4]
[ 1 73 1]
[ 0 1 55]]
FOLD 3 Number of neighbors 11
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 11
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.99 0.97 0.98 75
technology
0.98 0.96 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[68 1 1]
[ 2 73 0]
[ 2 0 54]]
FOLD 5 Number of neighbors 11
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 11
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.94 0.93 0.94 70
sport
0.97 0.99 0.98 75
technology
0.91 0.91 0.91 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[65 0 5]
[ 1 74 0]
[ 3 2 51]]
FOLD 7 Number of neighbors 11
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 2 1 52]]
Model accuracy (for 11 neighbours): 96.02% (+/- 1.47%)
FOLD 1 Number of neighbors 13
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.96 0.97 71
sport
0.99 1.00 0.99 76
technology
0.95 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 1 1 54]]
FOLD 2 Number of neighbors 13
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.97 0.98 75
technology
0.92 0.98 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 0 4]
[ 1 73 1]
[ 0 1 55]]
FOLD 3 Number of neighbors 13
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 13
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
1.00 0.97 0.99 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 2 0 54]]
FOLD 5 Number of neighbors 13
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 13
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.94 0.93 0.94 70
sport
0.97 0.99 0.98 75
technology
0.91 0.91 0.91 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[65 0 5]
[ 1 74 0]
[ 3 2 51]]
FOLD 7 Number of neighbors 13
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 2 1 52]]
Model accuracy (for 13 neighbours): 96.12% (+/- 1.43%)
FOLD 1 Number of neighbors 15
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.94 0.96 71
sport
0.99 1.00 0.99 76
technology
0.93 0.96 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 1 1 54]]
FOLD 2 Number of neighbors 15
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 0.97 0.98 75
technology
0.90 0.98 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[65 0 5]
[ 1 73 1]
[ 0 1 55]]
FOLD 3 Number of neighbors 15
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 15
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
1.00 0.97 0.99 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 2 0 54]]
FOLD 5 Number of neighbors 15
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 1 74 0]
[ 0 1 55]]
FOLD 6 Number of neighbors 15
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.94 0.91 0.93 70
sport
0.96 0.99 0.97 75
technology
0.91 0.91 0.91 56
avg / total 0.94 0.94 0.94 201
Confusion Matrix (Fold 6 ):
[[64 1 5]
[ 1 74 0]
[ 3 2 51]]
FOLD 7 Number of neighbors 15
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.99 0.99 0.99 75
technology
0.93 0.95 0.94 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 0 4]
[ 1 74 0]
[ 2 1 52]]
Model accuracy (for 15 neighbours): 96.16% (+/- 1.41%)
FOLD 1 Number of neighbors 17
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.94 0.96 71
sport
1.00 1.00 1.00 76
technology
0.93 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 1 0 55]]
FOLD 2 Number of neighbors 17
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 0.97 0.98 75
technology
0.90 0.98 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[65 0 5]
[ 1 73 1]
[ 0 1 55]]
FOLD 3 Number of neighbors 17
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 1.00 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 17
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
1.00 0.97 0.99 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 2 0 54]]
FOLD 5 Number of neighbors 17
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 1 74 0]
[ 1 1 54]]
FOLD 6 Number of neighbors 17
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.93 0.89 0.91 70
sport
0.94 0.99 0.96 75
technology
0.91 0.89 0.90 56
avg / total 0.93 0.93 0.92 201
Confusion Matrix (Fold 6 ):
[[62 3 5]
[ 1 74 0]
[ 4 2 50]]
FOLD 7 Number of neighbors 17
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.94 0.94 0.94 70
sport
0.99 0.99 0.99 75
technology
0.93 0.93 0.93 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[66 0 4]
[ 1 74 0]
[ 3 1 51]]
Model accuracy (for 17 neighbours): 96.17% (+/- 1.44%)
FOLD 1 Number of neighbors 19
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.94 0.96 71
sport
1.00 1.00 1.00 76
technology
0.93 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 1 0 55]]
FOLD 2 Number of neighbors 19
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 0.97 0.98 75
technology
0.90 0.98 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[65 0 5]
[ 1 73 1]
[ 0 1 55]]
FOLD 3 Number of neighbors 19
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 19
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
1.00 0.97 0.99 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 2 0 54]]
FOLD 5 Number of neighbors 19
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 1 74 0]
[ 1 1 54]]
FOLD 6 Number of neighbors 19
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.94 0.89 0.91 70
sport
0.94 0.99 0.96 75
technology
0.91 0.91 0.91 56
avg / total 0.93 0.93 0.93 201
Confusion Matrix (Fold 6 ):
[[62 3 5]
[ 1 74 0]
[ 3 2 51]]
FOLD 7 Number of neighbors 19
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.93 0.96 0.95 55
avg / total 0.97 0.96 0.97 200
Confusion Matrix (Fold 7 ):
[[66 0 4]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 19 neighbours): 96.19% (+/- 1.45%)
FOLD 1 Number of neighbors 21
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.96 0.97 71
sport
0.99 1.00 0.99 76
technology
0.95 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 1 1 54]]
FOLD 2 Number of neighbors 21
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 0.97 0.97 75
technology
0.92 0.96 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 0 4]
[ 1 73 1]
[ 0 2 54]]
FOLD 3 Number of neighbors 21
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 21
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.97 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 2 73 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 21
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.99 0.99 0.99 75
technology
0.93 0.96 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 5 ):
[[65 1 4]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 21
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.95 0.90 0.93 70
sport
0.95 1.00 0.97 75
technology
0.91 0.91 0.91 56
avg / total 0.94 0.94 0.94 201
Confusion Matrix (Fold 6 ):
[[63 2 5]
[ 0 75 0]
[ 3 2 51]]
FOLD 7 Number of neighbors 21
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 0.96 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 21 neighbours): 96.22% (+/- 1.43%)
FOLD 1 Number of neighbors 23
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.96 0.97 71
sport
1.00 1.00 1.00 76
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 1 0 55]]
FOLD 2 Number of neighbors 23
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 0.97 0.97 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 1 3]
[ 1 73 1]
[ 0 1 55]]
FOLD 3 Number of neighbors 23
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 23
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.97 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 2 73 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 23
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.99 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 23
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.95 0.90 0.93 70
sport
0.95 1.00 0.97 75
technology
0.91 0.91 0.91 56
avg / total 0.94 0.94 0.94 201
Confusion Matrix (Fold 6 ):
[[63 2 5]
[ 0 75 0]
[ 3 2 51]]
FOLD 7 Number of neighbors 23
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.96 0.95 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 23 neighbours): 96.25% (+/- 1.41%)
FOLD 1 Number of neighbors 25
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
1.00 1.00 1.00 76
technology
0.95 1.00 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 25
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.96 0.97 0.97 75
technology
0.93 0.96 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 1 3]
[ 1 73 1]
[ 0 2 54]]
FOLD 3 Number of neighbors 25
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 25
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.97 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 2 73 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 25
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.99 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 25
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.94 0.87 0.90 70
sport
0.94 1.00 0.97 75
technology
0.89 0.89 0.89 56
avg / total 0.93 0.93 0.92 201
Confusion Matrix (Fold 6 ):
[[61 3 6]
[ 0 75 0]
[ 4 2 50]]
FOLD 7 Number of neighbors 25
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 2 1 52]]
Model accuracy (for 25 neighbours): 96.25% (+/- 1.44%)
FOLD 1 Number of neighbors 27
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
1.00 1.00 1.00 76
technology
0.95 1.00 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 27
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 0.97 0.97 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 73 1]
[ 0 1 55]]
FOLD 3 Number of neighbors 27
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 27
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.97 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 2 73 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 27
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.99 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 27
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.94 0.89 0.91 70
sport
0.94 1.00 0.97 75
technology
0.91 0.89 0.90 56
avg / total 0.93 0.93 0.93 201
Confusion Matrix (Fold 6 ):
[[62 3 5]
[ 0 75 0]
[ 4 2 50]]
FOLD 7 Number of neighbors 27
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 2 1 52]]
Model accuracy (for 27 neighbours): 96.26% (+/- 1.45%)
FOLD 1 Number of neighbors 29
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
1.00 1.00 1.00 76
technology
0.95 1.00 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 29
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 0.97 0.97 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 73 1]
[ 0 1 55]]
FOLD 3 Number of neighbors 29
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 29
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 5 Number of neighbors 29
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.99 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 29
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.94 0.90 0.92 70
sport
0.94 1.00 0.97 75
technology
0.93 0.89 0.91 56
avg / total 0.94 0.94 0.93 201
Confusion Matrix (Fold 6 ):
[[63 3 4]
[ 0 75 0]
[ 4 2 50]]
FOLD 7 Number of neighbors 29
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.96 0.95 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 29 neighbours): 96.29% (+/- 1.45%)
FOLD 1 Number of neighbors 31
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
1.00 1.00 1.00 76
technology
0.95 1.00 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 31
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.96 0.97 0.97 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 73 1]
[ 0 2 54]]
FOLD 3 Number of neighbors 31
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 31
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 31
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 31
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.94 0.90 0.92 70
sport
0.94 1.00 0.97 75
technology
0.93 0.89 0.91 56
avg / total 0.94 0.94 0.93 201
Confusion Matrix (Fold 6 ):
[[63 3 4]
[ 0 75 0]
[ 4 2 50]]
FOLD 7 Number of neighbors 31
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 31 neighbours): 96.31% (+/- 1.46%)
FOLD 1 Number of neighbors 33
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 33
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.96 0.97 0.97 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 73 1]
[ 0 2 54]]
FOLD 3 Number of neighbors 33
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 33
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 33
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 33
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.94 0.90 0.92 70
sport
0.94 1.00 0.97 75
technology
0.93 0.89 0.91 56
avg / total 0.94 0.94 0.93 201
Confusion Matrix (Fold 6 ):
[[63 3 4]
[ 0 75 0]
[ 4 2 50]]
FOLD 7 Number of neighbors 33
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 33 neighbours): 96.33% (+/- 1.46%)
FOLD 1 Number of neighbors 35
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 35
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 0.97 0.97 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 73 1]
[ 0 1 55]]
FOLD 3 Number of neighbors 35
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 35
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 35
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 35
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.95 0.89 0.92 70
sport
0.95 1.00 0.97 75
technology
0.91 0.93 0.92 56
avg / total 0.94 0.94 0.94 201
Confusion Matrix (Fold 6 ):
[[62 3 5]
[ 0 75 0]
[ 3 1 52]]
FOLD 7 Number of neighbors 35
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.96 0.95 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 35 neighbours): 96.37% (+/- 1.46%)
FOLD 1 Number of neighbors 37
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 37
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 0.97 0.97 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 73 1]
[ 0 1 55]]
FOLD 3 Number of neighbors 37
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 37
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 37
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 37
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.95 0.90 0.93 70
sport
0.95 1.00 0.97 75
technology
0.93 0.93 0.93 56
avg / total 0.95 0.95 0.94 201
Confusion Matrix (Fold 6 ):
[[63 3 4]
[ 0 75 0]
[ 3 1 52]]
FOLD 7 Number of neighbors 37
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.96 0.95 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 37 neighbours): 96.40% (+/- 1.46%)
FOLD 1 Number of neighbors 39
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 39
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 0.97 0.97 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 73 1]
[ 0 1 55]]
FOLD 3 Number of neighbors 39
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.97 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 39
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 39
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 39
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.95 0.90 0.93 70
sport
0.95 1.00 0.97 75
technology
0.93 0.93 0.93 56
avg / total 0.95 0.95 0.94 201
Confusion Matrix (Fold 6 ):
[[63 3 4]
[ 0 75 0]
[ 3 1 52]]
FOLD 7 Number of neighbors 39
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 39 neighbours): 96.43% (+/- 1.45%)
FOLD 1 Number of neighbors 41
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 41
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 0.97 0.97 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 73 1]
[ 0 1 55]]
FOLD 3 Number of neighbors 41
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 1.00 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 41
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 41
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 41
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.95 0.90 0.93 70
sport
0.95 1.00 0.97 75
technology
0.93 0.93 0.93 56
avg / total 0.95 0.95 0.94 201
Confusion Matrix (Fold 6 ):
[[63 3 4]
[ 0 75 0]
[ 3 1 52]]
FOLD 7 Number of neighbors 41
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 41 neighbours): 96.46% (+/- 1.45%)
FOLD 1 Number of neighbors 43
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 43
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.97 0.98 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 73 1]
[ 0 0 56]]
FOLD 3 Number of neighbors 43
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 43
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 43
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 43
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.95 0.90 0.93 70
sport
0.95 1.00 0.97 75
technology
0.93 0.93 0.93 56
avg / total 0.95 0.95 0.94 201
Confusion Matrix (Fold 6 ):
[[63 3 4]
[ 0 75 0]
[ 3 1 52]]
FOLD 7 Number of neighbors 43
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 43 neighbours): 96.49% (+/- 1.44%)
FOLD 1 Number of neighbors 45
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
1.00 1.00 1.00 76
technology
0.95 1.00 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 45
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 3 Number of neighbors 45
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 45
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 45
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 45
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.96 0.91 0.93 70
sport
0.95 1.00 0.97 75
technology
0.95 0.93 0.94 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[64 3 3]
[ 0 75 0]
[ 3 1 52]]
FOLD 7 Number of neighbors 45
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 45 neighbours): 96.51% (+/- 1.44%)
FOLD 1 Number of neighbors 47
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 47
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.97 0.98 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 73 1]
[ 0 0 56]]
FOLD 3 Number of neighbors 47
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 47
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 47
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 47
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[65 2 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 47
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 47 neighbours): 96.54% (+/- 1.42%)
FOLD 1 Number of neighbors 49
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
0.99 1.00 0.99 76
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 49
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 0.99 0.98 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 74 0]
[ 0 1 55]]
FOLD 3 Number of neighbors 49
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 49
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 49
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 49
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[65 2 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 49
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 49 neighbours): 96.57% (+/- 1.41%)
FOLD 1 Number of neighbors 51
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
1.00 1.00 1.00 76
technology
0.95 1.00 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 51
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.96 0.99 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 74 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 51
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 51
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 51
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 51
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[65 2 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 51
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 51 neighbours): 96.59% (+/- 1.40%)
FOLD 1 Number of neighbors 53
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
0.99 1.00 0.99 76
technology
0.93 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 53
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.96 0.99 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 74 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 53
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 53
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 53
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 53
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[65 2 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 53
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 53 neighbours): 96.61% (+/- 1.38%)
FOLD 1 Number of neighbors 55
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
0.99 1.00 0.99 76
technology
0.93 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 55
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.96 0.99 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 74 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 55
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 55
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 55
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 55
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 55
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 55 neighbours): 96.62% (+/- 1.36%)
FOLD 1 Number of neighbors 57
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
0.99 1.00 0.99 76
technology
0.93 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 57
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 57
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 57
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 57
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 57
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 57
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 57 neighbours): 96.64% (+/- 1.35%)
FOLD 1 Number of neighbors 59
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
0.99 1.00 0.99 76
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 59
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 59
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 59
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 59
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 59
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 59
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 59 neighbours): 96.66% (+/- 1.33%)
FOLD 1 Number of neighbors 61
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
0.99 1.00 0.99 76
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 61
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 61
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
0.99 1.00 0.99 75
technology
0.98 0.98 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 61
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 61
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 61
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 61
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 61 neighbours): 96.67% (+/- 1.32%)
FOLD 1 Number of neighbors 63
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
0.99 1.00 0.99 76
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 63
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 63
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
0.99 1.00 0.99 75
technology
0.98 0.98 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 63
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 63
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 63
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 63
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 63 neighbours): 96.70% (+/- 1.32%)
FOLD 1 Number of neighbors 65
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
0.99 1.00 0.99 76
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 65
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.96 0.97 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 2 73 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 65
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 1.00 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 65
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 65
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 65
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 65
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 65 neighbours): 96.71% (+/- 1.31%)
FOLD 1 Number of neighbors 67
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
0.99 1.00 0.99 76
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 67
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.97 0.96 75
technology
0.96 0.96 0.96 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 67
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 1.00 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 67
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 67
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 67
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 67
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 67 neighbours): 96.73% (+/- 1.30%)
FOLD 1 Number of neighbors 69
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
0.99 1.00 0.99 76
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 69
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.97 0.96 75
technology
0.96 0.96 0.96 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 69
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 1.00 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 69
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 69
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
1.00 0.97 0.99 70
sport
1.00 1.00 1.00 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 0 75 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 69
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 69
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 69 neighbours): 96.75% (+/- 1.30%)
FOLD 1 Number of neighbors 71
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
0.99 1.00 0.99 76
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 71
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 71
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 1.00 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 71
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 71
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
1.00 0.96 0.98 70
sport
1.00 1.00 1.00 75
technology
0.95 1.00 0.97 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 71
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 71
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 71 neighbours): 96.76% (+/- 1.30%)
FOLD 1 Number of neighbors 73
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
0.99 1.00 0.99 76
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 73
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 73
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 1.00 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 73
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 73
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
1.00 0.96 0.98 70
sport
1.00 1.00 1.00 75
technology
0.95 1.00 0.97 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 73
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 73
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 73 neighbours): 96.78% (+/- 1.29%)
FOLD 1 Number of neighbors 75
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
0.99 1.00 0.99 76
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 75
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 75
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 1.00 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 75
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 75
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 1.00 1.00 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 75
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 75
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.96 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 1 52]]
Model accuracy (for 75 neighbours): 96.78% (+/- 1.28%)
FOLD 1 Number of neighbors 77
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
0.99 1.00 0.99 76
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 77
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.96 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 77
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 1.00 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 77
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 77
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
1.00 1.00 1.00 75
technology
0.95 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 0 75 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 77
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 77
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.96 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 1 52]]
Model accuracy (for 77 neighbours): 96.79% (+/- 1.27%)
FOLD 1 Number of neighbors 79
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
0.99 1.00 0.99 76
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 79
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 79
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 1.00 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 79
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 79
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 79
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 79
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.96 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 1 52]]
Model accuracy (for 79 neighbours): 96.79% (+/- 1.26%)
FOLD 1 Number of neighbors 81
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
0.99 1.00 0.99 76
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 81
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 81
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 1.00 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 81
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 81
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 81
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 81
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.96 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 1 52]]
Model accuracy (for 81 neighbours): 96.79% (+/- 1.25%)
FOLD 1 Number of neighbors 83
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
0.99 1.00 0.99 76
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 83
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 83
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 1.00 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 83
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 83
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 83
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 83
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.96 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 1 52]]
Model accuracy (for 83 neighbours): 96.80% (+/- 1.25%)
FOLD 1 Number of neighbors 85
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
0.99 1.00 0.99 76
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 85
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 85
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 1.00 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 85
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 85
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 85
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 85
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.96 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 1 52]]
Model accuracy (for 85 neighbours): 96.80% (+/- 1.24%)
FOLD 1 Number of neighbors 87
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
0.99 1.00 0.99 76
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 87
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 87
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 1.00 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 87
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 87
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 87
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 87
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.96 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 1 52]]
Model accuracy (for 87 neighbours): 96.80% (+/- 1.23%)
FOLD 1 Number of neighbors 89
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
0.99 1.00 0.99 76
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 89
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 89
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 1.00 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 89
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 89
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 89
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 89
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.96 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 1 52]]
Model accuracy (for 89 neighbours): 96.81% (+/- 1.23%)
FOLD 1 Number of neighbors 91
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 91
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 91
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 1.00 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 91
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 91
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 91
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 91
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.96 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 1 52]]
Model accuracy (for 91 neighbours): 96.81% (+/- 1.22%)
FOLD 1 Number of neighbors 93
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 93
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 93
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 1.00 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 93
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 93
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 93
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 93
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.96 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 1 52]]
Model accuracy (for 93 neighbours): 96.81% (+/- 1.22%)
FOLD 1 Number of neighbors 95
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 95
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 95
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 1.00 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 95
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 95
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 95
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 95
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.96 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 1 52]]
Model accuracy (for 95 neighbours): 96.82% (+/- 1.21%)
FOLD 1 Number of neighbors 97
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 97
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 97
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 1.00 0.99 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 97
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 97
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 97
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 97
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.96 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 1 52]]
Model accuracy (for 97 neighbours): 96.82% (+/- 1.21%)
FOLD 1 Number of neighbors 99
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 99
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 99
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 99
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 99
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 99
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 99
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.96 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 1 52]]
Model accuracy (for 99 neighbours): 96.82% (+/- 1.20%)
FOLD 1 Number of neighbors 101
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 101
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 101
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 101
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 101
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.94 0.96 0.95 70
sport
1.00 0.99 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 3 0 53]]
FOLD 6 Number of neighbors 101
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 101
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.96 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 1 52]]
Model accuracy (for 101 neighbours): 96.82% (+/- 1.20%)
FOLD 1 Number of neighbors 103
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 103
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 103
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 103
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 103
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.94 0.96 0.95 70
sport
1.00 0.99 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 3 0 53]]
FOLD 6 Number of neighbors 103
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 103
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.96 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 1 52]]
Model accuracy (for 103 neighbours): 96.82% (+/- 1.19%)
FOLD 1 Number of neighbors 105
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 105
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 105
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 105
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 105
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.94 0.96 0.95 70
sport
1.00 0.99 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 3 0 53]]
FOLD 6 Number of neighbors 105
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 105
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.96 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 1 52]]
Model accuracy (for 105 neighbours): 96.82% (+/- 1.19%)
FOLD 1 Number of neighbors 107
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 107
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 107
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 107
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 5 Number of neighbors 107
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.94 0.96 0.95 70
sport
1.00 0.99 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 3 0 53]]
FOLD 6 Number of neighbors 107
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 107
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.96 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 1 52]]
Model accuracy (for 107 neighbours): 96.81% (+/- 1.18%)
FOLD 1 Number of neighbors 109
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 109
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 109
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 109
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 5 Number of neighbors 109
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.94 0.96 0.95 70
sport
1.00 0.99 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 3 0 53]]
FOLD 6 Number of neighbors 109
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 109
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.96 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 1 52]]
Model accuracy (for 109 neighbours): 96.81% (+/- 1.18%)
FOLD 1 Number of neighbors 111
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 111
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 111
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 111
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 111
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.94 0.96 0.95 70
sport
1.00 0.99 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 3 0 53]]
FOLD 6 Number of neighbors 111
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 111
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.96 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 1 52]]
Model accuracy (for 111 neighbours): 96.81% (+/- 1.18%)
FOLD 1 Number of neighbors 113
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 113
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 113
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 113
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 5 Number of neighbors 113
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 3 0 53]]
FOLD 6 Number of neighbors 113
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 113
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.96 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 1 52]]
Model accuracy (for 113 neighbours): 96.80% (+/- 1.17%)
FOLD 1 Number of neighbors 115
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 115
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 115
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 115
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 115
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 3 0 53]]
FOLD 6 Number of neighbors 115
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 115
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.96 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 1 52]]
Model accuracy (for 115 neighbours): 96.80% (+/- 1.17%)
FOLD 1 Number of neighbors 117
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 117
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 117
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 117
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 117
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 3 0 53]]
FOLD 6 Number of neighbors 117
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 117
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.95 0.99 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 117 neighbours): 96.80% (+/- 1.17%)
FOLD 1 Number of neighbors 119
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 119
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 119
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 119
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 119
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.94 0.96 0.95 70
sport
1.00 0.99 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 3 0 53]]
FOLD 6 Number of neighbors 119
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 119
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.95 0.99 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 119 neighbours): 96.80% (+/- 1.16%)
FOLD 1 Number of neighbors 121
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.97 1.00 0.99 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 121
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 121
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 121
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 5 Number of neighbors 121
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.94 0.96 0.95 70
sport
1.00 0.99 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 3 0 53]]
FOLD 6 Number of neighbors 121
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 121
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.95 0.99 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 121 neighbours): 96.79% (+/- 1.16%)
FOLD 1 Number of neighbors 123
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.97 1.00 0.99 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 123
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 123
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 123
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 5 Number of neighbors 123
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 123
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 123
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.95 0.99 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 123 neighbours): 96.79% (+/- 1.16%)
FOLD 1 Number of neighbors 125
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.97 1.00 0.99 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 125
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 125
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.96 0.93 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 2 52]]
FOLD 4 Number of neighbors 125
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 5 Number of neighbors 125
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 125
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 125
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.95 0.99 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 125 neighbours): 96.78% (+/- 1.16%)
FOLD 1 Number of neighbors 127
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.97 1.00 0.99 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 127
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 127
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.96 0.93 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 2 52]]
FOLD 4 Number of neighbors 127
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 127
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 127
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 127
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.95 0.99 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 127 neighbours): 96.78% (+/- 1.16%)
FOLD 1 Number of neighbors 129
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.97 1.00 0.99 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 129
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 129
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.96 0.93 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 2 52]]
FOLD 4 Number of neighbors 129
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 129
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 129
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 129
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.95 0.99 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 129 neighbours): 96.77% (+/- 1.16%)
FOLD 1 Number of neighbors 131
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.97 1.00 0.99 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 131
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 131
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.96 0.93 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 2 52]]
FOLD 4 Number of neighbors 131
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 131
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 131
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 131
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.95 0.99 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 131 neighbours): 96.77% (+/- 1.16%)
FOLD 1 Number of neighbors 133
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.97 1.00 0.99 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 133
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 133
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.96 0.93 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 2 52]]
FOLD 4 Number of neighbors 133
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 133
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 133
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 3 3 50]]
FOLD 7 Number of neighbors 133
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.95 0.99 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 133 neighbours): 96.76% (+/- 1.16%)
FOLD 1 Number of neighbors 135
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.97 1.00 0.99 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 135
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 135
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.96 0.93 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 2 52]]
FOLD 4 Number of neighbors 135
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 135
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 135
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 3 3 50]]
FOLD 7 Number of neighbors 135
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.96 0.99 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 135 neighbours): 96.75% (+/- 1.16%)
FOLD 1 Number of neighbors 137
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.97 1.00 0.99 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 137
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 137
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.96 0.93 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 2 52]]
FOLD 4 Number of neighbors 137
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 137
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 137
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 3 3 50]]
FOLD 7 Number of neighbors 137
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.96 0.99 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 137 neighbours): 96.75% (+/- 1.15%)
FOLD 1 Number of neighbors 139
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.97 1.00 0.99 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 139
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 139
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.96 0.93 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 2 52]]
FOLD 4 Number of neighbors 139
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 139
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 139
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.94 1.00 0.97 75
technology
0.94 0.88 0.91 56
avg / total 0.95 0.95 0.94 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 3 4 49]]
FOLD 7 Number of neighbors 139
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.95 0.99 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 139 neighbours): 96.74% (+/- 1.16%)
FOLD 1 Number of neighbors 141
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.97 1.00 0.99 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 141
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 141
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.96 0.93 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 2 52]]
FOLD 4 Number of neighbors 141
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 141
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 141
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 141
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.96 0.99 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 141 neighbours): 96.73% (+/- 1.16%)
FOLD 1 Number of neighbors 143
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.97 1.00 0.99 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 143
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 143
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.96 0.93 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 2 52]]
FOLD 4 Number of neighbors 143
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.99 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 143
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 143
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 143
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.96 0.99 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 143 neighbours): 96.73% (+/- 1.16%)
FOLD 1 Number of neighbors 145
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.96 1.00 0.98 76
technology
0.96 0.95 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 145
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 145
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.95 1.00 0.97 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[67 2 1]
[ 0 75 0]
[ 2 2 52]]
FOLD 4 Number of neighbors 145
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.99 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 145
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 145
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 145
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.96 0.99 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 145 neighbours): 96.73% (+/- 1.15%)
FOLD 1 Number of neighbors 147
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.97 1.00 0.99 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 147
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 147
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.98 0.91 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[67 2 1]
[ 0 75 0]
[ 2 3 51]]
FOLD 4 Number of neighbors 147
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.99 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 147
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 147
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 147
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.96 0.99 0.97 75
technology
0.96 0.93 0.94 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 1 2]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 147 neighbours): 96.72% (+/- 1.15%)
FOLD 1 Number of neighbors 149
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.97 1.00 0.99 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 149
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 149
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.91 0.94 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 3 51]]
FOLD 4 Number of neighbors 149
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.99 0.99 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 149
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 149
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 149
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.96 0.99 0.97 75
technology
0.96 0.93 0.94 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 1 2]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 149 neighbours): 96.72% (+/- 1.15%)
FOLD 1 Number of neighbors 151
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.96 1.00 0.98 76
technology
0.96 0.95 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 151
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 151
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.98 0.91 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[67 2 1]
[ 0 75 0]
[ 2 3 51]]
FOLD 4 Number of neighbors 151
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.99 0.99 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 151
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 151
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 151
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.96 0.99 0.97 75
technology
0.96 0.93 0.94 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 1 2]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 151 neighbours): 96.72% (+/- 1.15%)
FOLD 1 Number of neighbors 153
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.96 1.00 0.98 76
technology
0.96 0.95 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 153
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 153
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.98 0.91 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[67 2 1]
[ 0 75 0]
[ 2 3 51]]
FOLD 4 Number of neighbors 153
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.99 0.99 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 153
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 153
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 153
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.99 0.98 75
technology
0.96 0.93 0.94 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[68 0 2]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 153 neighbours): 96.72% (+/- 1.15%)
FOLD 1 Number of neighbors 155
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.96 1.00 0.98 76
technology
0.96 0.95 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 155
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 155
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.98 0.91 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[67 2 1]
[ 0 75 0]
[ 2 3 51]]
FOLD 4 Number of neighbors 155
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.99 0.99 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 155
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 155
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 155
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.96 0.99 0.97 75
technology
0.96 0.93 0.94 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 1 2]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 155 neighbours): 96.71% (+/- 1.15%)
FOLD 1 Number of neighbors 157
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.96 1.00 0.98 76
technology
0.96 0.95 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 157
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 157
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.98 0.91 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[67 2 1]
[ 0 75 0]
[ 2 3 51]]
FOLD 4 Number of neighbors 157
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.99 0.99 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 157
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 157
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 157
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.96 0.99 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 157 neighbours): 96.71% (+/- 1.15%)
FOLD 1 Number of neighbors 159
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.96 1.00 0.98 76
technology
0.96 0.95 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 159
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.96 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 159
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.98 0.91 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[67 2 1]
[ 0 75 0]
[ 2 3 51]]
FOLD 4 Number of neighbors 159
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.99 0.99 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 159
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 159
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 159
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.96 0.99 0.97 75
technology
0.96 0.93 0.94 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 1 2]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 159 neighbours): 96.71% (+/- 1.15%)
FOLD 1 Number of neighbors 161
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.96 1.00 0.98 76
technology
0.96 0.95 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 161
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.96 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 161
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.95 1.00 0.97 75
technology
0.98 0.91 0.94 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 3 51]]
FOLD 4 Number of neighbors 161
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.99 0.99 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 161
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 161
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 161
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.96 0.99 0.97 75
technology
0.96 0.93 0.94 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 1 2]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 161 neighbours): 96.71% (+/- 1.15%)
FOLD 1 Number of neighbors 163
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.96 1.00 0.98 76
technology
0.96 0.95 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 163
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.96 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 163
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 163
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.99 0.99 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 163
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 163
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 163
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.91 0.93 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[67 1 2]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 163 neighbours): 96.71% (+/- 1.15%)
FOLD 1 Number of neighbors 165
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.96 1.00 0.98 76
technology
0.96 0.95 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 165
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 165
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 165
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.99 0.99 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 165
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 165
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.93 1.00 0.96 75
technology
0.94 0.88 0.91 56
avg / total 0.95 0.95 0.94 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 5 49]]
FOLD 7 Number of neighbors 165
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.99 0.97 75
technology
0.94 0.91 0.93 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 165 neighbours): 96.70% (+/- 1.15%)
FOLD 1 Number of neighbors 167
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.96 1.00 0.98 76
technology
0.96 0.95 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 167
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 167
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 167
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.99 0.99 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 167
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 167
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 167
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.96 0.99 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 167 neighbours): 96.70% (+/- 1.15%)
FOLD 1 Number of neighbors 169
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.96 1.00 0.98 76
technology
0.96 0.95 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 169
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 169
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 169
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.99 0.99 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 169
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 169
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 169
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.96 0.99 0.97 75
technology
0.96 0.93 0.94 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 1 2]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 169 neighbours): 96.70% (+/- 1.15%)
FOLD 1 Number of neighbors 171
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.95 1.00 0.97 76
technology
0.96 0.93 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 171
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 171
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 171
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.99 0.99 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 171
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 171
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 171
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.91 0.93 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[67 1 2]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 171 neighbours): 96.70% (+/- 1.14%)
FOLD 1 Number of neighbors 173
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 173
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 173
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.93 1.00 0.96 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 2 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 173
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.99 0.99 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 173
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 173
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 173
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.91 0.93 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[67 1 2]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 173 neighbours): 96.69% (+/- 1.14%)
FOLD 1 Number of neighbors 175
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 175
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 175
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.93 1.00 0.96 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 2 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 175
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.99 0.99 0.99 75
technology
1.00 0.96 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 175
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 175
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 175
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.91 0.93 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[67 1 2]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 175 neighbours): 96.69% (+/- 1.14%)
FOLD 1 Number of neighbors 177
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 177
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 177
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.93 1.00 0.96 75
technology
0.96 0.89 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 177
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.99 0.99 0.99 75
technology
1.00 0.96 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 177
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 177
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 177
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.91 0.93 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[67 1 2]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 177 neighbours): 96.69% (+/- 1.15%)
FOLD 1 Number of neighbors 179
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 179
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 179
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 179
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.99 0.99 0.99 75
technology
1.00 0.96 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 179
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 179
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 179
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.91 0.93 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[67 1 2]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 179 neighbours): 96.69% (+/- 1.15%)
FOLD 1 Number of neighbors 181
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 181
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 181
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 181
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.99 0.99 0.99 75
technology
1.00 0.96 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 181
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 181
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 181
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.96 0.99 0.97 75
technology
0.96 0.91 0.93 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[68 0 2]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 181 neighbours): 96.69% (+/- 1.15%)
FOLD 1 Number of neighbors 183
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 183
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 183
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 183
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.99 0.99 0.99 75
technology
1.00 0.96 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 183
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 183
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 183
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.91 0.93 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[67 1 2]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 183 neighbours): 96.68% (+/- 1.15%)
FOLD 1 Number of neighbors 185
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 185
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.96 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 185
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.93 1.00 0.96 75
technology
0.96 0.89 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 185
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.99 0.99 0.99 75
technology
1.00 0.96 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 185
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 185
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 185
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.96 0.99 0.97 75
technology
0.96 0.91 0.93 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[68 0 2]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 185 neighbours): 96.68% (+/- 1.15%)
FOLD 1 Number of neighbors 187
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 187
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.96 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 187
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.93 1.00 0.96 75
technology
0.96 0.89 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 187
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.99 0.99 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 187
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 187
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 187
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.99 0.97 75
technology
0.94 0.91 0.93 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 187 neighbours): 96.68% (+/- 1.15%)
FOLD 1 Number of neighbors 189
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 189
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.96 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 189
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.93 1.00 0.96 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 2 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 189
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.99 0.99 0.99 75
technology
1.00 0.96 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 189
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 189
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 189
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.99 0.97 75
technology
0.94 0.91 0.93 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 189 neighbours): 96.68% (+/- 1.15%)
FOLD 1 Number of neighbors 191
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 191
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 191
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.93 1.00 0.96 75
technology
0.96 0.89 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 191
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.99 0.99 0.99 75
technology
1.00 0.96 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 191
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 191
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 191
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.91 0.93 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[67 1 2]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 191 neighbours): 96.68% (+/- 1.15%)
FOLD 1 Number of neighbors 193
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 193
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 193
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.93 1.00 0.96 75
technology
0.96 0.89 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 193
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.99 0.99 0.99 75
technology
1.00 0.96 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 193
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 193
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 193
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.91 0.93 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[67 1 2]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 193 neighbours): 96.67% (+/- 1.15%)
FOLD 1 Number of neighbors 195
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 195
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 195
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 195
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.99 0.99 0.99 75
technology
1.00 0.96 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 195
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 195
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 195
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.96 0.99 0.97 75
technology
0.98 0.91 0.94 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[69 0 1]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 195 neighbours): 96.67% (+/- 1.15%)
FOLD 1 Number of neighbors 197
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 197
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 197
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 197
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.99 0.99 0.99 75
technology
1.00 0.96 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 197
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 197
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 197
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.95 0.99 0.97 75
technology
0.98 0.91 0.94 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[68 1 1]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 197 neighbours): 96.67% (+/- 1.15%)
FOLD 1 Number of neighbors 199
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 199
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 199
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 199
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.99 0.99 0.99 75
technology
1.00 0.96 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 199
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 199
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 199
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.95 0.99 0.97 75
technology
0.98 0.91 0.94 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[68 1 1]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 199 neighbours): 96.67% (+/- 1.15%)
FOLD 1 Number of neighbors 201
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 201
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 201
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 201
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.99 0.99 0.99 75
technology
1.00 0.96 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 201
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 201
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 201
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.95 0.99 0.97 75
technology
0.98 0.91 0.94 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[68 1 1]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 201 neighbours): 96.67% (+/- 1.15%)
FOLD 1 Number of neighbors 203
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 203
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 203
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 203
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.99 0.99 0.99 75
technology
1.00 0.96 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 203
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 203
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 203
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.95 0.99 0.97 75
technology
0.98 0.91 0.94 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[68 1 1]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 203 neighbours): 96.67% (+/- 1.15%)
FOLD 1 Number of neighbors 205
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 205
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 205
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 205
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.99 0.99 0.99 75
technology
1.00 0.96 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 205
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 205
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 205
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.96 0.99 0.97 75
technology
0.98 0.91 0.94 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[69 0 1]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 205 neighbours): 96.67% (+/- 1.15%)
FOLD 1 Number of neighbors 207
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 207
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 207
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 207
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.99 0.99 0.99 75
technology
1.00 0.96 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 207
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 207
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 207
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.96 0.99 0.97 75
technology
0.98 0.91 0.94 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[69 0 1]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 207 neighbours): 96.67% (+/- 1.15%)
FOLD 1 Number of neighbors 209
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 209
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 209
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 209
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 209
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 3 0 53]]
FOLD 6 Number of neighbors 209
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 209
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.95 0.99 0.97 75
technology
0.98 0.91 0.94 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[68 1 1]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 209 neighbours): 96.67% (+/- 1.15%)
FOLD 1 Number of neighbors 211
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 211
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 211
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 211
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 211
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 3 0 53]]
FOLD 6 Number of neighbors 211
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 211
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.96 0.99 0.97 75
technology
0.98 0.91 0.94 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[69 0 1]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 211 neighbours): 96.67% (+/- 1.15%)
FOLD 1 Number of neighbors 213
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 213
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.96 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 213
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 213
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 213
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 213
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 213
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.96 0.99 0.97 75
technology
1.00 0.91 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 213 neighbours): 96.67% (+/- 1.15%)
FOLD 1 Number of neighbors 215
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 215
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 215
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 215
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 215
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 3 0 53]]
FOLD 6 Number of neighbors 215
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 215
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.96 0.99 0.97 75
technology
1.00 0.91 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 215 neighbours): 96.67% (+/- 1.15%)
FOLD 1 Number of neighbors 217
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 217
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 217
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 217
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 217
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 217
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 217
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.96 0.99 0.97 75
technology
1.00 0.91 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 217 neighbours): 96.67% (+/- 1.15%)
FOLD 1 Number of neighbors 219
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 219
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 219
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 219
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 219
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 219
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 219
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.96 0.99 0.97 75
technology
1.00 0.93 0.96 55
avg / total 0.98 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 1 3 51]]
Model accuracy (for 219 neighbours): 96.67% (+/- 1.15%)
FOLD 1 Number of neighbors 221
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 221
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 221
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 221
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 221
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 221
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 221
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.96 0.99 0.97 75
technology
1.00 0.93 0.96 55
avg / total 0.98 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 1 3 51]]
Model accuracy (for 221 neighbours): 96.67% (+/- 1.15%)
FOLD 1 Number of neighbors 223
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 223
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 223
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 223
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 223
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 223
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 223
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.96 0.99 0.97 75
technology
1.00 0.91 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 223 neighbours): 96.67% (+/- 1.14%)
FOLD 1 Number of neighbors 225
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 225
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 225
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 225
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 225
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
1.00 0.99 0.99 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 225
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 225
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.96 0.99 0.97 75
technology
0.98 0.93 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[69 0 1]
[ 1 74 0]
[ 1 3 51]]
Model accuracy (for 225 neighbours): 96.67% (+/- 1.14%)
FOLD 1 Number of neighbors 227
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 227
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 227
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 227
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 227
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
1.00 0.99 0.99 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 227
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 227
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.96 0.99 0.97 75
technology
0.98 0.91 0.94 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[69 0 1]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 227 neighbours): 96.67% (+/- 1.14%)
FOLD 1 Number of neighbors 229
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 229
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 229
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 229
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 229
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
1.00 0.99 0.99 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 229
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 229
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.96 0.99 0.97 75
technology
1.00 0.91 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 229 neighbours): 96.67% (+/- 1.14%)
FOLD 1 Number of neighbors 231
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 231
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 231
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 231
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 231
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
1.00 0.99 0.99 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 231
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 231
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.96 0.99 0.97 75
technology
1.00 0.91 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 231 neighbours): 96.67% (+/- 1.14%)
FOLD 1 Number of neighbors 233
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 233
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 233
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 233
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 233
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
1.00 0.99 0.99 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 233
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 233
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.96 0.99 0.97 75
technology
1.00 0.91 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 233 neighbours): 96.67% (+/- 1.14%)
FOLD 1 Number of neighbors 235
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 235
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 235
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 235
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 235
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.95 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 2 1 53]]
FOLD 6 Number of neighbors 235
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 235
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.96 0.99 0.97 75
technology
1.00 0.91 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 235 neighbours): 96.68% (+/- 1.14%)
FOLD 1 Number of neighbors 237
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 237
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 237
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 237
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 237
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.95 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 2 1 53]]
FOLD 6 Number of neighbors 237
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 237
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.96 0.99 0.97 75
technology
1.00 0.91 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 237 neighbours): 96.68% (+/- 1.14%)
FOLD 1 Number of neighbors 239
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 239
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 239
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 239
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 239
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 239
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 239
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.96 0.99 0.97 75
technology
1.00 0.91 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 239 neighbours): 96.68% (+/- 1.14%)
FOLD 1 Number of neighbors 241
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 241
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 241
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 241
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 241
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 241
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 241
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.96 0.99 0.97 75
technology
1.00 0.91 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 241 neighbours): 96.68% (+/- 1.14%)
FOLD 1 Number of neighbors 243
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 243
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 243
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 243
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 243
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 243
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 243
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.96 0.99 0.97 75
technology
1.00 0.91 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 243 neighbours): 96.68% (+/- 1.13%)
FOLD 1 Number of neighbors 245
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 245
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 245
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 245
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 245
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 245
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 245
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.96 0.99 0.97 75
technology
1.00 0.91 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 245 neighbours): 96.68% (+/- 1.13%)
FOLD 1 Number of neighbors 247
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 247
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.97 0.97 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 247
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 247
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 247
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 247
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 247
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.96 0.99 0.97 75
technology
1.00 0.91 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 247 neighbours): 96.68% (+/- 1.13%)
FOLD 1 Number of neighbors 249
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 249
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.97 0.97 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 249
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 249
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 249
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 249
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 249
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.96 0.99 0.97 75
technology
1.00 0.91 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 249 neighbours): 96.68% (+/- 1.13%)
FOLD 1 Number of neighbors 251
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 251
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.97 0.97 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 251
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 251
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 251
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 251
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 251
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.96 0.99 0.97 75
technology
1.00 0.91 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 251 neighbours): 96.68% (+/- 1.13%)
FOLD 1 Number of neighbors 253
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 253
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.97 0.97 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 253
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 253
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 253
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 253
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 253
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.96 0.99 0.97 75
technology
1.00 0.91 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 253 neighbours): 96.68% (+/- 1.13%)
FOLD 1 Number of neighbors 255
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 255
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.97 0.97 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 255
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 255
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 255
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 255
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 255
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.96 0.99 0.97 75
technology
1.00 0.91 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 255 neighbours): 96.68% (+/- 1.12%)
FOLD 1 Number of neighbors 257
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 257
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.97 0.97 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 257
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 257
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 257
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 257
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 257
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.96 0.99 0.97 75
technology
1.00 0.91 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 257 neighbours): 96.69% (+/- 1.12%)
FOLD 1 Number of neighbors 259
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 259
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.97 0.97 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 259
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 259
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 259
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 259
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 259
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.96 0.99 0.97 75
technology
1.00 0.91 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 259 neighbours): 96.69% (+/- 1.12%)
FOLD 1 Number of neighbors 261
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 261
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.97 0.97 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 261
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 261
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 261
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 261
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 261
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.96 0.99 0.97 75
technology
1.00 0.91 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 261 neighbours): 96.69% (+/- 1.12%)
FOLD 1 Number of neighbors 263
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 263
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.97 0.97 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 263
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 263
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 263
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 263
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 263
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.96 0.99 0.97 75
technology
1.00 0.91 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 263 neighbours): 96.69% (+/- 1.12%)
FOLD 1 Number of neighbors 265
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 265
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.97 0.97 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 265
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 265
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.97 0.97 0.97 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 2 73 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 265
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 265
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 265
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.96 0.99 0.97 75
technology
1.00 0.91 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 265 neighbours): 96.69% (+/- 1.11%)
FOLD 1 Number of neighbors 267
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 267
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.97 0.97 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 267
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 267
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.97 0.97 0.97 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 2 73 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 267
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 267
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 267
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.95 1.00 0.97 70
sport
0.96 0.99 0.97 75
technology
1.00 0.89 0.94 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 3 3 49]]
Model accuracy (for 267 neighbours): 96.69% (+/- 1.11%)
FOLD 1 Number of neighbors 269
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 269
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.97 0.97 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 269
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 269
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.97 0.97 0.97 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 2 73 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 269
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 269
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 269
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.95 1.00 0.97 70
sport
0.96 0.99 0.97 75
technology
1.00 0.89 0.94 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 3 3 49]]
Model accuracy (for 269 neighbours): 96.69% (+/- 1.11%)
FOLD 1 Number of neighbors 271
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.99 0.99 71
sport
0.95 0.99 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 1 75 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 271
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.97 0.97 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 271
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 271
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.97 0.97 0.97 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 2 73 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 271
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 271
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 271
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.95 1.00 0.97 70
sport
0.96 0.99 0.97 75
technology
1.00 0.89 0.94 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 3 3 49]]
Model accuracy (for 271 neighbours): 96.69% (+/- 1.11%)
FOLD 1 Number of neighbors 273
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.99 0.99 71
sport
0.95 0.99 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 1 75 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 273
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.97 0.97 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 273
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 273
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 273
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 273
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 273
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.95 1.00 0.97 70
sport
0.95 0.99 0.97 75
technology
1.00 0.87 0.93 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 3 4 48]]
Model accuracy (for 273 neighbours): 96.69% (+/- 1.10%)
FOLD 1 Number of neighbors 275
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.99 0.99 71
sport
0.95 0.99 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 1 75 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 275
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.97 0.97 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 275
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 275
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 275
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 275
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 275
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.95 1.00 0.97 70
sport
0.95 0.99 0.97 75
technology
1.00 0.87 0.93 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 3 4 48]]
Model accuracy (for 275 neighbours): 96.69% (+/- 1.10%)
FOLD 1 Number of neighbors 277
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.99 0.99 71
sport
0.95 0.99 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 1 75 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 277
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.97 0.97 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 277
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 277
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 277
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 277
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 277
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.95 1.00 0.97 70
sport
0.95 0.99 0.97 75
technology
1.00 0.87 0.93 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 3 4 48]]
Model accuracy (for 277 neighbours): 96.69% (+/- 1.10%)
FOLD 1 Number of neighbors 279
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.99 0.99 71
sport
0.95 0.99 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 1 75 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 279
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.97 0.97 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 279
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 279
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 279
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 279
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 279
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.93 1.00 0.97 70
sport
0.95 0.99 0.97 75
technology
1.00 0.85 0.92 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 4 4 47]]
Model accuracy (for 279 neighbours): 96.69% (+/- 1.10%)
FOLD 1 Number of neighbors 281
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 281
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.97 0.97 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 281
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 281
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 281
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 281
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 281
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.93 1.00 0.97 70
sport
0.95 0.99 0.97 75
technology
1.00 0.85 0.92 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 4 4 47]]
Model accuracy (for 281 neighbours): 96.69% (+/- 1.10%)
FOLD 1 Number of neighbors 283
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.99 0.99 71
sport
0.95 0.99 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 1 75 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 283
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.97 0.97 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 283
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 283
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 283
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 283
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 283
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.93 1.00 0.97 70
sport
0.95 0.99 0.97 75
technology
1.00 0.85 0.92 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 4 4 47]]
Model accuracy (for 283 neighbours): 96.69% (+/- 1.10%)
FOLD 1 Number of neighbors 285
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 285
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.97 0.97 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 285
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 285
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 285
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 285
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 285
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.93 1.00 0.97 70
sport
0.95 0.99 0.97 75
technology
1.00 0.85 0.92 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 4 4 47]]
Model accuracy (for 285 neighbours): 96.69% (+/- 1.09%)
FOLD 1 Number of neighbors 287
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 287
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.97 0.97 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 287
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 287
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 287
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 287
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 287
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.95 1.00 0.97 70
sport
0.95 0.99 0.97 75
technology
1.00 0.87 0.93 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 3 4 48]]
Model accuracy (for 287 neighbours): 96.69% (+/- 1.09%)
FOLD 1 Number of neighbors 289
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 289
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.97 0.97 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 289
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 289
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 289
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 289
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 289
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.95 1.00 0.97 70
sport
0.95 0.99 0.97 75
technology
1.00 0.87 0.93 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 3 4 48]]
Model accuracy (for 289 neighbours): 96.69% (+/- 1.09%)
FOLD 1 Number of neighbors 291
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 291
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.97 0.97 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 291
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 291
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 291
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 291
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 291
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.95 1.00 0.97 70
sport
0.94 0.99 0.96 75
technology
1.00 0.85 0.92 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 3 5 47]]
Model accuracy (for 291 neighbours): 96.69% (+/- 1.09%)
FOLD 1 Number of neighbors 293
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 293
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.97 0.97 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 293
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 293
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.97 0.97 0.97 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 2 73 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 293
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 293
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 293
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.95 1.00 0.97 70
sport
0.94 0.99 0.96 75
technology
1.00 0.85 0.92 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 3 5 47]]
Model accuracy (for 293 neighbours): 96.69% (+/- 1.09%)
FOLD 1 Number of neighbors 295
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 295
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.97 0.96 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 295
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 295
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.97 0.97 0.97 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 2 73 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 295
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 295
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 295
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.95 1.00 0.97 70
sport
0.94 0.99 0.96 75
technology
1.00 0.85 0.92 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 3 5 47]]
Model accuracy (for 295 neighbours): 96.69% (+/- 1.09%)
FOLD 1 Number of neighbors 297
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 297
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.97 0.96 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 297
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 297
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.97 0.97 0.97 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 2 73 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 297
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 297
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 297
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.95 1.00 0.97 70
sport
0.94 0.99 0.96 75
technology
1.00 0.85 0.92 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 3 5 47]]
Model accuracy (for 297 neighbours): 96.69% (+/- 1.09%)
FOLD 1 Number of neighbors 299
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 299
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.97 0.96 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 299
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 299
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 299
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 299
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 299
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.95 1.00 0.97 70
sport
0.94 0.99 0.96 75
technology
1.00 0.85 0.92 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 3 5 47]]
Model accuracy (for 299 neighbours): 96.69% (+/- 1.08%)
In [376]:
# changing to misclassification error
MSE_not_bal = [1-x/100 for x in k_model_accuracy_not_bal]
index_not_bal=MSE_not_bal.index(min(MSE_not_bal))
optimal_k_not_bal = neighbors[index_not_bal]
print ("The highest model accuracy",k_model_accuracy_not_bal[index_not_bal],"is achieved by using optimal number of neighbors %d" % optimal_k_not_bal)
# plot misclassification error vs k
plt.plot(neighbors, MSE_not_bal)
plt.xlabel('Number of Neighbors K')
plt.ylabel('Misclassification Error')
plt.show()
The highest model accuracy 96.82067537908272 is achieved by using optimal number of neighbors 97
In [377]:
cvscores_ngram_not_bal = []
k_model_accuracy_ngram_not_bal=[]
kfold = StratifiedKFold(n_splits=7, shuffle=True, random_state=seed)
for k in neighbors:
fold=0
model = KNeighborsClassifier(n_neighbors=k,algorithm='brute', metric='cosine')
for train, test in kfold.split(X_term_weighting_ngram, class_labels):
fold+=1
print('FOLD',fold, 'Number of neighbors', k)
labels_train=[]
for i in range(len(train)):
labels_train.append(class_labels[train[i]])
labels_test=[]
for i in range(len(test)):
labels_test.append(class_labels[test[i]])
model.fit(X_term_weighting_ngram[train], labels_train)
#Evaluate the Model; Use the test dataset to evaluate the model
print('\n\n ****** Test Data ******** (Fold',fold,'):')
# Make a set of predictions for the validation data
predicted = model.predict(X_term_weighting_ngram[test])
# Print performance details
print(metrics.classification_report(labels_test, predicted))
# Print confusion matrix
print('Confusion Matrix (Fold',fold,'):')
print(metrics.confusion_matrix(labels_test, predicted))
cvscores_ngram_not_bal.append(accuracy_score(labels_test, predicted) * 100)
print("\n\n Model accuracy (for",k," neighbours): %.2f%% (+/- %.2f%%)" % (numpy.mean(cvscores_ngram_not_bal), numpy.std(cvscores_ngram_not_bal)))
k_model_accuracy_ngram_not_bal.append(numpy.mean(cvscores_ngram_not_bal))
FOLD 1 Number of neighbors 1
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.94 0.94 0.94 71
sport
1.00 0.96 0.98 76
technology
0.92 0.96 0.94 56
avg / total 0.96 0.96 0.96 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 2 73 1]
[ 2 0 54]]
FOLD 2 Number of neighbors 1
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
1.00 0.87 0.93 70
sport
0.96 0.99 0.97 75
technology
0.87 0.98 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 2 ):
[[61 2 7]
[ 0 74 1]
[ 0 1 55]]
FOLD 3 Number of neighbors 1
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.99 0.95 0.97 75
technology
0.96 0.98 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 3 71 1]
[ 1 0 55]]
FOLD 4 Number of neighbors 1
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.93 0.97 0.95 70
sport
0.99 0.93 0.96 75
technology
0.95 0.96 0.96 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 4 ):
[[68 1 1]
[ 3 70 2]
[ 2 0 54]]
FOLD 5 Number of neighbors 1
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.97 0.97 0.97 75
technology
0.90 0.96 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 5 ):
[[64 1 5]
[ 1 73 1]
[ 1 1 54]]
FOLD 6 Number of neighbors 1
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.93 0.93 0.93 70
sport
0.95 0.97 0.96 75
technology
0.94 0.91 0.93 56
avg / total 0.94 0.94 0.94 201
Confusion Matrix (Fold 6 ):
[[65 3 2]
[ 1 73 1]
[ 4 1 51]]
FOLD 7 Number of neighbors 1
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.90 0.89 0.89 70
sport
0.91 0.96 0.94 75
technology
0.90 0.85 0.88 55
avg / total 0.90 0.91 0.90 200
Confusion Matrix (Fold 7 ):
[[62 4 4]
[ 2 72 1]
[ 5 3 47]]
Model accuracy (for 1 neighbours): 94.53% (+/- 1.80%)
FOLD 1 Number of neighbors 3
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.96 0.96 71
sport
0.99 0.97 0.98 76
technology
0.93 0.96 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 1 74 1]
[ 1 1 54]]
FOLD 2 Number of neighbors 3
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.90 0.93 70
sport
0.97 0.96 0.97 75
technology
0.89 0.98 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 2 ):
[[63 1 6]
[ 2 72 1]
[ 0 1 55]]
FOLD 3 Number of neighbors 3
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 3 ):
[[67 2 1]
[ 1 74 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 3
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
1.00 0.96 0.98 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 72 1]
[ 1 0 55]]
FOLD 5 Number of neighbors 3
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 0.99 0.99 75
technology
0.92 0.98 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[65 0 5]
[ 1 74 0]
[ 0 1 55]]
FOLD 6 Number of neighbors 3
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.94 0.93 0.94 70
sport
0.96 0.99 0.97 75
technology
0.93 0.91 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[65 1 4]
[ 1 74 0]
[ 3 2 51]]
FOLD 7 Number of neighbors 3
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.94 0.91 0.93 70
sport
0.95 0.97 0.96 75
technology
0.93 0.93 0.93 55
avg / total 0.94 0.94 0.94 200
Confusion Matrix (Fold 7 ):
[[64 2 4]
[ 2 73 0]
[ 2 2 51]]
Model accuracy (for 3 neighbours): 95.20% (+/- 1.74%)
FOLD 1 Number of neighbors 5
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.94 0.96 71
sport
0.99 0.99 0.99 76
technology
0.93 0.96 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 1 75 0]
[ 1 1 54]]
FOLD 2 Number of neighbors 5
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.97 0.97 0.97 75
technology
0.92 0.98 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[65 1 4]
[ 1 73 1]
[ 0 1 55]]
FOLD 3 Number of neighbors 5
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.97 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 4 Number of neighbors 5
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
1.00 0.95 0.97 75
technology
0.96 0.98 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 3 71 1]
[ 1 0 55]]
FOLD 5 Number of neighbors 5
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 5
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.94 0.93 0.94 70
sport
0.97 0.99 0.98 75
technology
0.91 0.91 0.91 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[65 0 5]
[ 1 74 0]
[ 3 2 51]]
FOLD 7 Number of neighbors 5
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.97 0.97 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 2 73 0]
[ 1 1 53]]
Model accuracy (for 5 neighbours): 95.59% (+/- 1.62%)
FOLD 1 Number of neighbors 7
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.93 0.95 71
sport
0.97 0.99 0.98 76
technology
0.93 0.96 0.95 56
avg / total 0.96 0.96 0.96 203
Confusion Matrix (Fold 1 ):
[[66 1 4]
[ 1 75 0]
[ 1 1 54]]
FOLD 2 Number of neighbors 7
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.97 0.98 75
technology
0.92 0.98 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 0 4]
[ 1 73 1]
[ 0 1 55]]
FOLD 3 Number of neighbors 7
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 4 Number of neighbors 7
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.93 0.99 0.96 70
sport
1.00 0.95 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 3 71 1]
[ 2 0 54]]
FOLD 5 Number of neighbors 7
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 1 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 7
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.93 0.90 0.91 70
sport
0.96 0.99 0.97 75
technology
0.89 0.89 0.89 56
avg / total 0.93 0.93 0.93 201
Confusion Matrix (Fold 6 ):
[[63 1 6]
[ 1 74 0]
[ 4 2 50]]
FOLD 7 Number of neighbors 7
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 0.97 0.97 75
technology
0.93 0.98 0.96 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 1 4]
[ 2 73 0]
[ 0 1 54]]
Model accuracy (for 7 neighbours): 95.75% (+/- 1.61%)
FOLD 1 Number of neighbors 9
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.94 0.96 71
sport
0.97 1.00 0.99 76
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[67 1 3]
[ 0 76 0]
[ 1 1 54]]
FOLD 2 Number of neighbors 9
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.97 0.98 75
technology
0.92 0.98 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 0 4]
[ 1 73 1]
[ 0 1 55]]
FOLD 3 Number of neighbors 9
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 0.99 0.99 75
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 74 1]
[ 0 0 56]]
FOLD 4 Number of neighbors 9
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.93 0.99 0.96 70
sport
1.00 0.95 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 3 71 1]
[ 2 0 54]]
FOLD 5 Number of neighbors 9
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 1 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 9
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.94 0.91 0.93 70
sport
0.96 0.99 0.97 75
technology
0.91 0.91 0.91 56
avg / total 0.94 0.94 0.94 201
Confusion Matrix (Fold 6 ):
[[64 1 5]
[ 1 74 0]
[ 3 2 51]]
FOLD 7 Number of neighbors 9
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.99 0.99 0.99 75
technology
0.96 0.96 0.96 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[68 0 2]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 9 neighbours): 95.95% (+/- 1.58%)
FOLD 1 Number of neighbors 11
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.99 1.00 0.99 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 1 1 54]]
FOLD 2 Number of neighbors 11
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
1.00 0.97 0.99 75
technology
0.92 1.00 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 0 4]
[ 1 73 1]
[ 0 0 56]]
FOLD 3 Number of neighbors 11
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 11
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
1.00 0.96 0.98 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 72 1]
[ 2 0 54]]
FOLD 5 Number of neighbors 11
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 0 75 0]
[ 0 1 55]]
FOLD 6 Number of neighbors 11
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.94 0.91 0.93 70
sport
0.96 0.99 0.97 75
technology
0.91 0.91 0.91 56
avg / total 0.94 0.94 0.94 201
Confusion Matrix (Fold 6 ):
[[64 1 5]
[ 1 74 0]
[ 3 2 51]]
FOLD 7 Number of neighbors 11
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.99 0.99 0.99 75
technology
0.93 0.95 0.94 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 0 4]
[ 1 74 0]
[ 2 1 52]]
Model accuracy (for 11 neighbours): 96.08% (+/- 1.56%)
FOLD 1 Number of neighbors 13
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.96 0.97 71
sport
0.99 1.00 0.99 76
technology
0.95 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 1 1 54]]
FOLD 2 Number of neighbors 13
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 0.97 0.97 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 1 3]
[ 1 73 1]
[ 0 1 55]]
FOLD 3 Number of neighbors 13
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 13
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
1.00 0.97 0.99 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 2 0 54]]
FOLD 5 Number of neighbors 13
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 1 2]
[ 1 74 0]
[ 1 1 54]]
FOLD 6 Number of neighbors 13
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.94 0.91 0.93 70
sport
0.96 0.99 0.97 75
technology
0.91 0.91 0.91 56
avg / total 0.94 0.94 0.94 201
Confusion Matrix (Fold 6 ):
[[64 1 5]
[ 1 74 0]
[ 3 2 51]]
FOLD 7 Number of neighbors 13
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.93 0.96 0.95 55
avg / total 0.97 0.96 0.97 200
Confusion Matrix (Fold 7 ):
[[66 0 4]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 13 neighbours): 96.16% (+/- 1.52%)
FOLD 1 Number of neighbors 15
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.96 0.97 71
sport
0.99 1.00 0.99 76
technology
0.95 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 1 1 54]]
FOLD 2 Number of neighbors 15
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 0.97 0.97 75
technology
0.90 0.98 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[64 1 5]
[ 1 73 1]
[ 0 1 55]]
FOLD 3 Number of neighbors 15
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 15
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
1.00 0.97 0.99 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 2 0 54]]
FOLD 5 Number of neighbors 15
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.97 0.99 0.98 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[65 1 4]
[ 1 74 0]
[ 0 1 55]]
FOLD 6 Number of neighbors 15
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.93 0.91 0.92 70
sport
0.96 0.99 0.97 75
technology
0.91 0.89 0.90 56
avg / total 0.93 0.94 0.94 201
Confusion Matrix (Fold 6 ):
[[64 1 5]
[ 1 74 0]
[ 4 2 50]]
FOLD 7 Number of neighbors 15
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.99 0.99 0.99 75
technology
0.93 0.95 0.94 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 0 4]
[ 1 74 0]
[ 2 1 52]]
Model accuracy (for 15 neighbours): 96.18% (+/- 1.50%)
FOLD 1 Number of neighbors 17
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.96 0.97 71
sport
1.00 1.00 1.00 76
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 1 0 55]]
FOLD 2 Number of neighbors 17
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 0.97 0.97 75
technology
0.90 0.98 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[64 1 5]
[ 1 73 1]
[ 0 1 55]]
FOLD 3 Number of neighbors 17
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 17
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
1.00 0.97 0.99 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 2 0 54]]
FOLD 5 Number of neighbors 17
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 1 74 0]
[ 1 1 54]]
FOLD 6 Number of neighbors 17
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.94 0.90 0.92 70
sport
0.95 0.99 0.97 75
technology
0.91 0.91 0.91 56
avg / total 0.94 0.94 0.93 201
Confusion Matrix (Fold 6 ):
[[63 2 5]
[ 1 74 0]
[ 3 2 51]]
FOLD 7 Number of neighbors 17
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 2 1 52]]
Model accuracy (for 17 neighbours): 96.21% (+/- 1.50%)
FOLD 1 Number of neighbors 19
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
1.00 1.00 1.00 76
technology
0.95 1.00 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 19
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.97 0.97 0.97 75
technology
0.92 0.98 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[65 1 4]
[ 1 73 1]
[ 0 1 55]]
FOLD 3 Number of neighbors 19
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 1.00 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 19
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
1.00 0.97 0.99 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 19
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 0.99 0.98 75
technology
0.93 0.96 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 5 ):
[[65 1 4]
[ 1 74 0]
[ 1 1 54]]
FOLD 6 Number of neighbors 19
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.94 0.90 0.92 70
sport
0.95 0.99 0.97 75
technology
0.91 0.91 0.91 56
avg / total 0.94 0.94 0.93 201
Confusion Matrix (Fold 6 ):
[[63 2 5]
[ 1 74 0]
[ 3 2 51]]
FOLD 7 Number of neighbors 19
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 0.96 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 19 neighbours): 96.26% (+/- 1.52%)
FOLD 1 Number of neighbors 21
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
1.00 1.00 1.00 76
technology
0.95 1.00 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 21
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.97 0.97 0.97 75
technology
0.92 0.98 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[65 1 4]
[ 1 73 1]
[ 0 1 55]]
FOLD 3 Number of neighbors 21
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 21
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.97 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 2 73 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 21
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.99 0.99 0.99 75
technology
0.93 0.96 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 5 ):
[[65 1 4]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 21
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.95 0.90 0.93 70
sport
0.95 1.00 0.97 75
technology
0.91 0.91 0.91 56
avg / total 0.94 0.94 0.94 201
Confusion Matrix (Fold 6 ):
[[63 2 5]
[ 0 75 0]
[ 3 2 51]]
FOLD 7 Number of neighbors 21
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 2 1 52]]
Model accuracy (for 21 neighbours): 96.29% (+/- 1.50%)
FOLD 1 Number of neighbors 23
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
1.00 1.00 1.00 76
technology
0.95 1.00 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 23
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 0.99 0.98 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 74 0]
[ 0 1 55]]
FOLD 3 Number of neighbors 23
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 23
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.97 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 2 73 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 23
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.99 0.99 0.99 75
technology
0.93 0.96 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 5 ):
[[65 1 4]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 23
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.95 0.90 0.93 70
sport
0.95 1.00 0.97 75
technology
0.91 0.91 0.91 56
avg / total 0.94 0.94 0.94 201
Confusion Matrix (Fold 6 ):
[[63 2 5]
[ 0 75 0]
[ 3 2 51]]
FOLD 7 Number of neighbors 23
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 2 1 52]]
Model accuracy (for 23 neighbours): 96.33% (+/- 1.49%)
FOLD 1 Number of neighbors 25
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 25
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 0.99 0.98 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 74 0]
[ 0 1 55]]
FOLD 3 Number of neighbors 25
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 25
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.97 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 2 73 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 25
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.99 0.99 0.99 75
technology
0.93 0.96 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 5 ):
[[65 1 4]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 25
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.96 0.91 0.93 70
sport
0.95 1.00 0.97 75
technology
0.93 0.91 0.92 56
avg / total 0.95 0.95 0.94 201
Confusion Matrix (Fold 6 ):
[[64 2 4]
[ 0 75 0]
[ 3 2 51]]
FOLD 7 Number of neighbors 25
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.96 0.95 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 25 neighbours): 96.36% (+/- 1.47%)
FOLD 1 Number of neighbors 27
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
1.00 1.00 1.00 76
technology
0.95 1.00 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 27
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 0.99 0.98 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 74 0]
[ 0 1 55]]
FOLD 3 Number of neighbors 27
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 27
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.97 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 2 73 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 27
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.99 0.99 0.99 75
technology
0.93 0.96 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 5 ):
[[65 1 4]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 27
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[65 2 3]
[ 0 75 0]
[ 3 2 51]]
FOLD 7 Number of neighbors 27
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.96 0.95 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 27 neighbours): 96.40% (+/- 1.45%)
FOLD 1 Number of neighbors 29
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
1.00 1.00 1.00 76
technology
0.95 1.00 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 29
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 0.99 0.98 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 74 0]
[ 0 1 55]]
FOLD 3 Number of neighbors 29
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 29
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 29
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.99 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 29
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.94 0.91 0.93 70
sport
0.95 1.00 0.97 75
technology
0.93 0.89 0.91 56
avg / total 0.94 0.94 0.94 201
Confusion Matrix (Fold 6 ):
[[64 2 4]
[ 0 75 0]
[ 4 2 50]]
FOLD 7 Number of neighbors 29
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 29 neighbours): 96.42% (+/- 1.45%)
FOLD 1 Number of neighbors 31
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 31
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 0.97 0.97 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 73 1]
[ 0 1 55]]
FOLD 3 Number of neighbors 31
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 31
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 5 Number of neighbors 31
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 1 74 0]
[ 1 1 54]]
FOLD 6 Number of neighbors 31
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.94 0.91 0.93 70
sport
0.96 1.00 0.98 75
technology
0.93 0.91 0.92 56
avg / total 0.94 0.95 0.94 201
Confusion Matrix (Fold 6 ):
[[64 2 4]
[ 0 75 0]
[ 4 1 51]]
FOLD 7 Number of neighbors 31
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 31 neighbours): 96.44% (+/- 1.43%)
FOLD 1 Number of neighbors 33
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 33
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 0.97 0.97 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 73 1]
[ 0 1 55]]
FOLD 3 Number of neighbors 33
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.97 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 33
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 5 Number of neighbors 33
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 1 74 0]
[ 1 1 54]]
FOLD 6 Number of neighbors 33
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.94 0.94 0.94 70
sport
0.96 1.00 0.98 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 4 2 50]]
FOLD 7 Number of neighbors 33
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.95 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 33 neighbours): 96.46% (+/- 1.41%)
FOLD 1 Number of neighbors 35
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 35
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 0.97 0.97 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 73 1]
[ 0 1 55]]
FOLD 3 Number of neighbors 35
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.97 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 35
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 5 Number of neighbors 35
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 1 74 0]
[ 1 1 54]]
FOLD 6 Number of neighbors 35
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.94 0.91 0.93 70
sport
0.95 1.00 0.97 75
technology
0.93 0.89 0.91 56
avg / total 0.94 0.94 0.94 201
Confusion Matrix (Fold 6 ):
[[64 2 4]
[ 0 75 0]
[ 4 2 50]]
FOLD 7 Number of neighbors 35
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.95 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 35 neighbours): 96.46% (+/- 1.41%)
FOLD 1 Number of neighbors 37
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 37
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 0.97 0.97 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 73 1]
[ 0 1 55]]
FOLD 3 Number of neighbors 37
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 37
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 37
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 1 74 0]
[ 0 1 55]]
FOLD 6 Number of neighbors 37
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.94 0.91 0.93 70
sport
0.96 1.00 0.98 75
technology
0.93 0.91 0.92 56
avg / total 0.94 0.95 0.94 201
Confusion Matrix (Fold 6 ):
[[64 2 4]
[ 0 75 0]
[ 4 1 51]]
FOLD 7 Number of neighbors 37
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.95 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 37 neighbours): 96.48% (+/- 1.40%)
FOLD 1 Number of neighbors 39
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
1.00 1.00 1.00 76
technology
0.95 1.00 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 39
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 0.99 0.98 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 74 0]
[ 0 1 55]]
FOLD 3 Number of neighbors 39
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 39
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 39
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 1 74 0]
[ 1 1 54]]
FOLD 6 Number of neighbors 39
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.94 0.90 0.92 70
sport
0.95 1.00 0.97 75
technology
0.93 0.91 0.92 56
avg / total 0.94 0.94 0.94 201
Confusion Matrix (Fold 6 ):
[[63 3 4]
[ 0 75 0]
[ 4 1 51]]
FOLD 7 Number of neighbors 39
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.95 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 39 neighbours): 96.49% (+/- 1.40%)
FOLD 1 Number of neighbors 41
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
1.00 1.00 1.00 76
technology
0.95 1.00 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 41
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 0.99 0.98 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 74 0]
[ 0 1 55]]
FOLD 3 Number of neighbors 41
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 41
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 41
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 0 75 0]
[ 0 1 55]]
FOLD 6 Number of neighbors 41
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.96 0.91 0.93 70
sport
0.96 1.00 0.98 75
technology
0.93 0.93 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[64 2 4]
[ 0 75 0]
[ 3 1 52]]
FOLD 7 Number of neighbors 41
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.95 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 41 neighbours): 96.50% (+/- 1.39%)
FOLD 1 Number of neighbors 43
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 43
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 0.99 0.98 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 74 0]
[ 0 1 55]]
FOLD 3 Number of neighbors 43
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 43
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 43
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 0 75 0]
[ 1 1 54]]
FOLD 6 Number of neighbors 43
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.96 0.91 0.93 70
sport
0.96 1.00 0.98 75
technology
0.93 0.93 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[64 2 4]
[ 0 75 0]
[ 3 1 52]]
FOLD 7 Number of neighbors 43
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.95 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 43 neighbours): 96.51% (+/- 1.38%)
FOLD 1 Number of neighbors 45
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 45
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 0.99 0.98 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 74 0]
[ 0 1 55]]
FOLD 3 Number of neighbors 45
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 45
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 45
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 45
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.96 1.00 0.98 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[64 2 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 45
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.95 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 45 neighbours): 96.53% (+/- 1.36%)
FOLD 1 Number of neighbors 47
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
1.00 1.00 1.00 76
technology
0.95 1.00 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 47
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 0.99 0.98 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 74 0]
[ 0 1 55]]
FOLD 3 Number of neighbors 47
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 47
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 47
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 47
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[65 2 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 47
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.95 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 47 neighbours): 96.55% (+/- 1.35%)
FOLD 1 Number of neighbors 49
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
1.00 1.00 1.00 76
technology
0.95 1.00 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 49
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 0.99 0.98 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 74 0]
[ 0 1 55]]
FOLD 3 Number of neighbors 49
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 49
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 49
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 49
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 49
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.95 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 49 neighbours): 96.58% (+/- 1.35%)
FOLD 1 Number of neighbors 51
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
0.99 1.00 0.99 76
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 51
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 0.99 0.98 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 74 0]
[ 0 1 55]]
FOLD 3 Number of neighbors 51
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 51
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 51
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 51
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 51
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.95 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 51 neighbours): 96.60% (+/- 1.33%)
FOLD 1 Number of neighbors 53
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 53
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.96 0.99 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 74 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 53
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 53
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 53
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 53
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 53
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.95 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 53 neighbours): 96.61% (+/- 1.32%)
FOLD 1 Number of neighbors 55
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
0.99 1.00 0.99 76
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 55
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.96 0.99 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 74 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 55
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 55
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 55
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 55
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 55
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.95 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 55 neighbours): 96.63% (+/- 1.31%)
FOLD 1 Number of neighbors 57
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
0.99 1.00 0.99 76
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 57
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.96 0.99 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 74 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 57
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 57
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 57
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 57
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 57
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.95 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 57 neighbours): 96.64% (+/- 1.30%)
FOLD 1 Number of neighbors 59
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
0.99 1.00 0.99 76
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 59
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.96 0.99 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 74 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 59
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 59
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 59
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 59
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 59
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.95 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 59 neighbours): 96.65% (+/- 1.29%)
FOLD 1 Number of neighbors 61
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
1.00 1.00 1.00 76
technology
0.95 1.00 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 61
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.96 0.99 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 74 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 61
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 61
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 61
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
1.00 0.96 0.98 70
sport
1.00 1.00 1.00 75
technology
0.95 1.00 0.97 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 61
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 61
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.95 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 61 neighbours): 96.67% (+/- 1.28%)
FOLD 1 Number of neighbors 63
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
1.00 1.00 1.00 76
technology
0.95 1.00 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 63
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 63
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 63
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 63
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 1.00 1.00 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 63
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 63
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.95 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 63 neighbours): 96.68% (+/- 1.28%)
FOLD 1 Number of neighbors 65
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
1.00 1.00 1.00 76
technology
0.95 1.00 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 65
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 65
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.97 1.00 0.99 75
technology
0.93 0.96 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 65
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 65
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
1.00 0.96 0.98 70
sport
1.00 1.00 1.00 75
technology
0.95 1.00 0.97 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 65
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 65
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.95 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 65 neighbours): 96.69% (+/- 1.27%)
FOLD 1 Number of neighbors 67
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
1.00 1.00 1.00 76
technology
0.95 1.00 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 67
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 67
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 67
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 67
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
1.00 0.97 0.99 70
sport
1.00 1.00 1.00 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 0 75 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 67
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 67
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.95 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 67 neighbours): 96.70% (+/- 1.27%)
FOLD 1 Number of neighbors 69
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
0.99 1.00 0.99 76
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 69
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 69
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 69
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 69
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
1.00 0.97 0.99 70
sport
1.00 1.00 1.00 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 0 75 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 69
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 69
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.95 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 69 neighbours): 96.72% (+/- 1.27%)
FOLD 1 Number of neighbors 71
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
0.99 1.00 0.99 76
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 71
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 71
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 71
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 71
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 1.00 1.00 75
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 0 75 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 71
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 71
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.95 0.99 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 71 neighbours): 96.72% (+/- 1.26%)
FOLD 1 Number of neighbors 73
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
0.99 1.00 0.99 76
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 73
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 73
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 73
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 73
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 1.00 1.00 75
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 0 75 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 73
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 73
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.95 0.99 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 73 neighbours): 96.73% (+/- 1.26%)
FOLD 1 Number of neighbors 75
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
0.99 1.00 0.99 76
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 75
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 75
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 75
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 75
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 1.00 1.00 75
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 0 75 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 75
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 75
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.95 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 75 neighbours): 96.74% (+/- 1.25%)
FOLD 1 Number of neighbors 77
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
0.99 1.00 0.99 76
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 77
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 77
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.97 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 77
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 77
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 1.00 1.00 75
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 0 75 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 77
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 77
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.95 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 77 neighbours): 96.75% (+/- 1.25%)
FOLD 1 Number of neighbors 79
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
0.99 1.00 0.99 76
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 79
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 79
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 79
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[66 1 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 79
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 1.00 1.00 75
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 0 75 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 79
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 79
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.95 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 79 neighbours): 96.76% (+/- 1.24%)
FOLD 1 Number of neighbors 81
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 81
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 81
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 81
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[66 1 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 81
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 1.00 1.00 75
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 0 75 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 81
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 81
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.95 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 81 neighbours): 96.77% (+/- 1.24%)
FOLD 1 Number of neighbors 83
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 83
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 83
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 83
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[66 1 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 83
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 1.00 1.00 75
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 0 75 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 83
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 83
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.95 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 83 neighbours): 96.77% (+/- 1.24%)
FOLD 1 Number of neighbors 85
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 85
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 85
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 85
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 85
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 1.00 1.00 75
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 0 75 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 85
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 85
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.95 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 85 neighbours): 96.78% (+/- 1.23%)
FOLD 1 Number of neighbors 87
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 87
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 87
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 87
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 87
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
1.00 0.97 0.99 70
sport
1.00 1.00 1.00 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 0 75 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 87
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 87
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.95 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 87 neighbours): 96.79% (+/- 1.23%)
FOLD 1 Number of neighbors 89
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 89
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 89
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 89
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 89
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 1.00 1.00 75
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 0 75 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 89
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 89
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.95 0.99 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 89 neighbours): 96.79% (+/- 1.23%)
FOLD 1 Number of neighbors 91
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 91
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 91
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 91
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 91
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 91
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 91
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.95 0.99 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 91 neighbours): 96.79% (+/- 1.23%)
FOLD 1 Number of neighbors 93
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 93
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 93
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.97 1.00 0.99 75
technology
0.93 0.96 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 93
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 93
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 93
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 93
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.95 0.99 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 93 neighbours): 96.79% (+/- 1.23%)
FOLD 1 Number of neighbors 95
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 95
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 95
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.97 1.00 0.99 75
technology
0.93 0.96 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 95
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 95
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 95
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 95
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.95 0.99 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 95 neighbours): 96.79% (+/- 1.23%)
FOLD 1 Number of neighbors 97
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 97
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 97
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.97 1.00 0.99 75
technology
0.93 0.96 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 97
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 97
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 97
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 97
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.96 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 1 52]]
Model accuracy (for 97 neighbours): 96.79% (+/- 1.23%)
FOLD 1 Number of neighbors 99
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 99
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 99
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.97 1.00 0.99 75
technology
0.93 0.96 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 99
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 99
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 99
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 99
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.95 0.99 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 99 neighbours): 96.79% (+/- 1.22%)
FOLD 1 Number of neighbors 101
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 101
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 101
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.97 1.00 0.99 75
technology
0.93 0.96 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 101
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 101
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 101
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 101
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.96 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 1 52]]
Model accuracy (for 101 neighbours): 96.79% (+/- 1.22%)
FOLD 1 Number of neighbors 103
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 103
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 103
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.97 1.00 0.99 75
technology
0.93 0.96 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 103
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 103
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 103
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 1 54]]
FOLD 7 Number of neighbors 103
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.96 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 1 52]]
Model accuracy (for 103 neighbours): 96.79% (+/- 1.22%)
FOLD 1 Number of neighbors 105
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 105
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 105
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.96 1.00 0.98 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 105
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 105
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 105
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 1 54]]
FOLD 7 Number of neighbors 105
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.95 0.99 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 105 neighbours): 96.79% (+/- 1.21%)
FOLD 1 Number of neighbors 107
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 107
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 107
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 107
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 107
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 107
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 1 54]]
FOLD 7 Number of neighbors 107
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.95 0.99 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 107 neighbours): 96.79% (+/- 1.21%)
FOLD 1 Number of neighbors 109
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 109
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 109
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 109
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 109
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 109
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 109
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.96 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 1 52]]
Model accuracy (for 109 neighbours): 96.79% (+/- 1.21%)
FOLD 1 Number of neighbors 111
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 111
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 111
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 111
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 111
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 111
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 111
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.96 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 1 52]]
Model accuracy (for 111 neighbours): 96.79% (+/- 1.21%)
FOLD 1 Number of neighbors 113
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 113
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 113
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 113
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 113
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 113
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 113
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.96 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 1 52]]
Model accuracy (for 113 neighbours): 96.79% (+/- 1.20%)
FOLD 1 Number of neighbors 115
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 115
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 115
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 115
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 115
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 115
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 1 54]]
FOLD 7 Number of neighbors 115
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.96 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 1 52]]
Model accuracy (for 115 neighbours): 96.79% (+/- 1.20%)
FOLD 1 Number of neighbors 117
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 117
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 117
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 117
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 1 1]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 117
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 117
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 1 54]]
FOLD 7 Number of neighbors 117
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.96 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 1 52]]
Model accuracy (for 117 neighbours): 96.80% (+/- 1.19%)
FOLD 1 Number of neighbors 119
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 119
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 119
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 119
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 1 1]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 119
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 119
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 1 54]]
FOLD 7 Number of neighbors 119
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.96 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 1 52]]
Model accuracy (for 119 neighbours): 96.80% (+/- 1.19%)
FOLD 1 Number of neighbors 121
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 121
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 121
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 121
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 1 1]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 121
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 121
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 1 54]]
FOLD 7 Number of neighbors 121
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.96 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 1 52]]
Model accuracy (for 121 neighbours): 96.80% (+/- 1.19%)
FOLD 1 Number of neighbors 123
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 123
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 123
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 123
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 1 1]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 123
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 123
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 1 54]]
FOLD 7 Number of neighbors 123
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.95 0.99 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 123 neighbours): 96.80% (+/- 1.19%)
FOLD 1 Number of neighbors 125
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 125
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 125
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 125
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 1 1]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 125
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 125
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 1 54]]
FOLD 7 Number of neighbors 125
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.95 0.99 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 125 neighbours): 96.80% (+/- 1.19%)
FOLD 1 Number of neighbors 127
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 127
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 127
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 127
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.99 0.99 0.99 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 1 1]
[ 1 74 0]
[ 2 0 54]]
FOLD 5 Number of neighbors 127
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 127
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 3 52]]
FOLD 7 Number of neighbors 127
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.95 0.99 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 127 neighbours): 96.80% (+/- 1.18%)
FOLD 1 Number of neighbors 129
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 129
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 129
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 129
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 1 1]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 129
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 129
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 3 52]]
FOLD 7 Number of neighbors 129
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.95 0.99 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 129 neighbours): 96.80% (+/- 1.18%)
FOLD 1 Number of neighbors 131
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 131
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 131
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 131
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 1 1]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 131
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 131
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 3 52]]
FOLD 7 Number of neighbors 131
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.95 0.99 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 131 neighbours): 96.80% (+/- 1.18%)
FOLD 1 Number of neighbors 133
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 133
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 133
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 133
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 1 1]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 133
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 133
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 3 52]]
FOLD 7 Number of neighbors 133
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.95 0.99 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 133 neighbours): 96.80% (+/- 1.18%)
FOLD 1 Number of neighbors 135
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 135
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 135
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 135
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 1 1]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 135
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 135
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 3 52]]
FOLD 7 Number of neighbors 135
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.95 0.99 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 135 neighbours): 96.79% (+/- 1.18%)
FOLD 1 Number of neighbors 137
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.97 1.00 0.99 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 137
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 137
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 137
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.99 0.98 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[68 1 1]
[ 1 74 0]
[ 2 1 53]]
FOLD 5 Number of neighbors 137
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 137
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 3 52]]
FOLD 7 Number of neighbors 137
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.95 0.99 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 137 neighbours): 96.79% (+/- 1.18%)
FOLD 1 Number of neighbors 139
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.97 1.00 0.99 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 139
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 139
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 139
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.97 0.99 0.98 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 1 1]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 139
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 139
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 3 52]]
FOLD 7 Number of neighbors 139
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.95 0.99 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 139 neighbours): 96.79% (+/- 1.18%)
FOLD 1 Number of neighbors 141
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.97 1.00 0.99 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 141
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 141
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.95 1.00 0.97 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 2 2 52]]
FOLD 4 Number of neighbors 141
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.97 0.99 0.98 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 1 1]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 141
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 141
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 3 52]]
FOLD 7 Number of neighbors 141
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.94 0.99 0.96 75
technology
0.94 0.91 0.93 55
avg / total 0.95 0.94 0.94 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 141 neighbours): 96.78% (+/- 1.18%)
FOLD 1 Number of neighbors 143
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.97 1.00 0.99 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 143
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 143
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.96 0.93 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 2 52]]
FOLD 4 Number of neighbors 143
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.97 0.99 0.98 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 1 1]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 143
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 143
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 3 52]]
FOLD 7 Number of neighbors 143
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.96 0.99 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 143 neighbours): 96.78% (+/- 1.17%)
FOLD 1 Number of neighbors 145
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.97 1.00 0.99 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 145
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 145
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.95 1.00 0.97 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 2 2 52]]
FOLD 4 Number of neighbors 145
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.97 0.99 0.98 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 1 1]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 145
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 145
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 3 52]]
FOLD 7 Number of neighbors 145
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.99 0.97 75
technology
0.94 0.91 0.93 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 145 neighbours): 96.77% (+/- 1.17%)
FOLD 1 Number of neighbors 147
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.96 1.00 0.98 76
technology
0.96 0.95 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 147
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 147
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.95 1.00 0.97 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 2 2 52]]
FOLD 4 Number of neighbors 147
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.97 0.99 0.98 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 1 1]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 147
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 147
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 3 52]]
FOLD 7 Number of neighbors 147
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.99 0.97 75
technology
0.94 0.91 0.93 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 147 neighbours): 96.77% (+/- 1.17%)
FOLD 1 Number of neighbors 149
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.96 1.00 0.98 76
technology
0.96 0.95 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 149
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 149
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.95 1.00 0.97 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 2 2 52]]
FOLD 4 Number of neighbors 149
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.97 0.99 0.98 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 1 1]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 149
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 149
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 4 51]]
FOLD 7 Number of neighbors 149
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.99 0.97 75
technology
0.94 0.91 0.93 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 149 neighbours): 96.76% (+/- 1.17%)
FOLD 1 Number of neighbors 151
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.96 1.00 0.98 76
technology
0.96 0.95 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 151
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 151
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.95 1.00 0.97 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 2 2 52]]
FOLD 4 Number of neighbors 151
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 2 1 53]]
FOLD 5 Number of neighbors 151
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 151
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 4 51]]
FOLD 7 Number of neighbors 151
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.99 0.97 75
technology
0.94 0.91 0.93 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 151 neighbours): 96.75% (+/- 1.17%)
FOLD 1 Number of neighbors 153
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.96 1.00 0.98 76
technology
0.96 0.95 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 153
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 153
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.94 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 4 Number of neighbors 153
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.99 0.98 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[68 1 1]
[ 1 74 0]
[ 2 1 53]]
FOLD 5 Number of neighbors 153
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 153
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 4 51]]
FOLD 7 Number of neighbors 153
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.99 0.97 75
technology
0.94 0.91 0.93 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 153 neighbours): 96.74% (+/- 1.17%)
FOLD 1 Number of neighbors 155
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.96 1.00 0.98 76
technology
0.96 0.95 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 155
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 155
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.94 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 4 Number of neighbors 155
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.99 0.98 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[68 1 1]
[ 1 74 0]
[ 2 1 53]]
FOLD 5 Number of neighbors 155
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 155
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 4 51]]
FOLD 7 Number of neighbors 155
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.99 0.97 75
technology
0.94 0.91 0.93 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 155 neighbours): 96.74% (+/- 1.17%)
FOLD 1 Number of neighbors 157
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.96 1.00 0.98 76
technology
0.96 0.95 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 157
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 157
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.91 0.94 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 3 51]]
FOLD 4 Number of neighbors 157
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.99 0.98 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[68 1 1]
[ 1 74 0]
[ 2 1 53]]
FOLD 5 Number of neighbors 157
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 157
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 3 52]]
FOLD 7 Number of neighbors 157
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.96 0.99 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 157 neighbours): 96.73% (+/- 1.16%)
FOLD 1 Number of neighbors 159
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.96 1.00 0.98 76
technology
0.96 0.95 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 159
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 159
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.91 0.94 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 3 51]]
FOLD 4 Number of neighbors 159
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.99 0.98 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[68 1 1]
[ 1 74 0]
[ 2 1 53]]
FOLD 5 Number of neighbors 159
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 159
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 3 52]]
FOLD 7 Number of neighbors 159
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.96 0.99 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 159 neighbours): 96.73% (+/- 1.16%)
FOLD 1 Number of neighbors 161
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.96 1.00 0.98 76
technology
0.96 0.95 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 161
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 161
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.91 0.94 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 3 51]]
FOLD 4 Number of neighbors 161
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.99 0.98 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[68 1 1]
[ 1 74 0]
[ 2 1 53]]
FOLD 5 Number of neighbors 161
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 161
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 3 52]]
FOLD 7 Number of neighbors 161
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.96 0.99 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 2 2 51]]
Model accuracy (for 161 neighbours): 96.72% (+/- 1.16%)
FOLD 1 Number of neighbors 163
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.96 1.00 0.98 76
technology
0.96 0.95 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 163
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 163
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.91 0.94 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 3 51]]
FOLD 4 Number of neighbors 163
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.97 0.99 0.98 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 1 1]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 163
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 163
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 3 52]]
FOLD 7 Number of neighbors 163
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.99 0.97 75
technology
0.94 0.91 0.93 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 163 neighbours): 96.72% (+/- 1.16%)
FOLD 1 Number of neighbors 165
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.96 1.00 0.98 76
technology
0.96 0.95 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 165
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 165
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.94 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 4 Number of neighbors 165
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.99 0.98 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[68 1 1]
[ 1 74 0]
[ 2 1 53]]
FOLD 5 Number of neighbors 165
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 165
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 3 52]]
FOLD 7 Number of neighbors 165
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.94 0.99 0.96 75
technology
0.96 0.89 0.92 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[67 1 2]
[ 1 74 0]
[ 2 4 49]]
Model accuracy (for 165 neighbours): 96.71% (+/- 1.16%)
FOLD 1 Number of neighbors 167
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.96 1.00 0.98 76
technology
0.96 0.95 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 167
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 167
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.94 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 4 Number of neighbors 167
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.97 0.99 0.98 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 1 1]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 167
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 167
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 3 52]]
FOLD 7 Number of neighbors 167
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.94 0.99 0.96 75
technology
0.96 0.89 0.92 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[67 1 2]
[ 1 74 0]
[ 2 4 49]]
Model accuracy (for 167 neighbours): 96.71% (+/- 1.15%)
FOLD 1 Number of neighbors 169
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.96 1.00 0.98 76
technology
0.96 0.95 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 169
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 169
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.91 0.94 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 3 51]]
FOLD 4 Number of neighbors 169
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.97 0.99 0.98 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 1 1]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 169
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 169
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 3 52]]
FOLD 7 Number of neighbors 169
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.91 0.93 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[67 1 2]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 169 neighbours): 96.71% (+/- 1.15%)
FOLD 1 Number of neighbors 171
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.96 1.00 0.98 76
technology
0.96 0.95 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 171
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 171
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.91 0.94 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 3 51]]
FOLD 4 Number of neighbors 171
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.97 0.99 0.98 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 1 1]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 171
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 171
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 3 52]]
FOLD 7 Number of neighbors 171
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.91 0.93 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[67 1 2]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 171 neighbours): 96.71% (+/- 1.15%)
FOLD 1 Number of neighbors 173
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.96 1.00 0.98 76
technology
0.96 0.95 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 173
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 173
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.94 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 4 Number of neighbors 173
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.97 0.99 0.98 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 1 1]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 173
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 173
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 3 52]]
FOLD 7 Number of neighbors 173
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.91 0.93 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[67 1 2]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 173 neighbours): 96.70% (+/- 1.15%)
FOLD 1 Number of neighbors 175
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.96 1.00 0.98 76
technology
0.96 0.95 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 175
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 175
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.94 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 4 Number of neighbors 175
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.97 0.99 0.98 75
technology
1.00 0.96 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 1 0]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 175
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 175
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 3 52]]
FOLD 7 Number of neighbors 175
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.99 0.97 75
technology
0.94 0.91 0.93 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 175 neighbours): 96.70% (+/- 1.15%)
FOLD 1 Number of neighbors 177
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.96 1.00 0.98 76
technology
0.96 0.95 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 177
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 177
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.94 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 4 Number of neighbors 177
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.97 0.99 0.98 75
technology
1.00 0.96 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 1 0]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 177
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 177
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 3 52]]
FOLD 7 Number of neighbors 177
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.99 0.97 75
technology
0.94 0.91 0.93 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 177 neighbours): 96.70% (+/- 1.15%)
FOLD 1 Number of neighbors 179
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.96 1.00 0.98 76
technology
0.96 0.95 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 179
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 179
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.91 0.94 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 3 51]]
FOLD 4 Number of neighbors 179
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.97 0.99 0.98 75
technology
1.00 0.96 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 1 0]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 179
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 179
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 179
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.99 0.97 75
technology
0.94 0.91 0.93 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 179 neighbours): 96.69% (+/- 1.15%)
FOLD 1 Number of neighbors 181
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.96 1.00 0.98 76
technology
0.96 0.95 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 181
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 181
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.94 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 4 Number of neighbors 181
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.97 0.99 0.98 75
technology
1.00 0.96 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 1 0]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 181
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 181
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 181
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.94 0.99 0.96 75
technology
0.94 0.89 0.92 55
avg / total 0.95 0.94 0.94 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 2 4 49]]
Model accuracy (for 181 neighbours): 96.69% (+/- 1.15%)
FOLD 1 Number of neighbors 183
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.96 1.00 0.98 76
technology
0.96 0.95 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 183
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 183
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.94 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 4 Number of neighbors 183
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.97 0.99 0.98 75
technology
1.00 0.96 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 1 0]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 183
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 183
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 183
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.94 0.99 0.96 75
technology
0.94 0.89 0.92 55
avg / total 0.95 0.94 0.94 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 2 4 49]]
Model accuracy (for 183 neighbours): 96.69% (+/- 1.15%)
FOLD 1 Number of neighbors 185
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.95 1.00 0.97 76
technology
0.96 0.93 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 185
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 185
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.93 1.00 0.96 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.94 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 185
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.97 0.99 0.98 75
technology
1.00 0.96 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 1 0]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 185
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 185
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 185
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.91 0.93 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[67 1 2]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 185 neighbours): 96.68% (+/- 1.15%)
FOLD 1 Number of neighbors 187
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.95 1.00 0.97 76
technology
0.96 0.93 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 187
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 187
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.93 1.00 0.96 75
technology
0.96 0.89 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 187
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.97 0.99 0.98 75
technology
1.00 0.96 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 1 0]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 187
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 187
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 187
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.91 0.93 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[67 1 2]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 187 neighbours): 96.68% (+/- 1.15%)
FOLD 1 Number of neighbors 189
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.96 1.00 0.98 76
technology
0.96 0.95 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 189
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 189
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.93 1.00 0.96 75
technology
0.96 0.89 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 189
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.97 0.99 0.98 75
technology
1.00 0.96 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 1 0]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 189
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 189
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 189
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.94 0.99 0.96 75
technology
0.94 0.89 0.92 55
avg / total 0.95 0.94 0.94 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 2 4 49]]
Model accuracy (for 189 neighbours): 96.67% (+/- 1.16%)
FOLD 1 Number of neighbors 191
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.96 1.00 0.98 76
technology
0.96 0.95 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 191
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 191
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.93 1.00 0.96 75
technology
0.96 0.89 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 191
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.97 0.99 0.98 75
technology
1.00 0.96 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 1 0]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 191
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 191
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 191
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.94 0.99 0.96 75
technology
0.98 0.89 0.93 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[68 1 1]
[ 1 74 0]
[ 2 4 49]]
Model accuracy (for 191 neighbours): 96.67% (+/- 1.16%)
FOLD 1 Number of neighbors 193
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.95 1.00 0.97 76
technology
0.96 0.93 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 193
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.96 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 193
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.93 1.00 0.96 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.94 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 193
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.97 0.99 0.98 75
technology
1.00 0.96 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 1 0]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 193
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 193
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 193
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.94 0.99 0.96 75
technology
0.98 0.89 0.93 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[68 1 1]
[ 1 74 0]
[ 2 4 49]]
Model accuracy (for 193 neighbours): 96.67% (+/- 1.16%)
FOLD 1 Number of neighbors 195
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.95 1.00 0.97 76
technology
0.96 0.93 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 195
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.96 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 195
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.93 1.00 0.96 75
technology
0.96 0.89 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 195
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.97 0.99 0.98 75
technology
1.00 0.96 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 1 0]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 195
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 195
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 195
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.94 0.99 0.96 75
technology
0.98 0.89 0.93 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[68 1 1]
[ 1 74 0]
[ 2 4 49]]
Model accuracy (for 195 neighbours): 96.67% (+/- 1.16%)
FOLD 1 Number of neighbors 197
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.95 1.00 0.97 76
technology
0.96 0.93 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 197
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.96 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 197
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.93 1.00 0.96 75
technology
0.96 0.89 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 197
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.97 0.99 0.98 75
technology
1.00 0.96 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 1 0]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 197
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 197
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 197
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.94 0.99 0.96 75
technology
0.98 0.89 0.93 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[68 1 1]
[ 1 74 0]
[ 2 4 49]]
Model accuracy (for 197 neighbours): 96.66% (+/- 1.16%)
FOLD 1 Number of neighbors 199
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.95 1.00 0.97 76
technology
0.96 0.93 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 199
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.96 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 199
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.93 1.00 0.96 75
technology
0.96 0.89 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 199
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.97 0.99 0.98 75
technology
1.00 0.96 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 1 0]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 199
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 199
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 199
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.94 0.99 0.96 75
technology
0.98 0.89 0.93 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[68 1 1]
[ 1 74 0]
[ 2 4 49]]
Model accuracy (for 199 neighbours): 96.66% (+/- 1.15%)
FOLD 1 Number of neighbors 201
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.95 1.00 0.97 76
technology
0.96 0.93 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 201
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.96 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 201
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.93 1.00 0.96 75
technology
0.96 0.89 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 201
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.99 0.99 0.99 75
technology
1.00 0.96 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 201
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 201
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 201
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.94 0.99 0.96 75
technology
0.96 0.89 0.92 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[67 1 2]
[ 1 74 0]
[ 2 4 49]]
Model accuracy (for 201 neighbours): 96.66% (+/- 1.16%)
FOLD 1 Number of neighbors 203
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.95 1.00 0.97 76
technology
0.96 0.93 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 203
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.96 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 203
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.93 1.00 0.96 75
technology
0.96 0.89 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 203
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.97 0.99 0.98 75
technology
1.00 0.96 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 1 0]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 203
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 203
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 203
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.94 0.99 0.96 75
technology
0.96 0.89 0.92 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[67 1 2]
[ 1 74 0]
[ 2 4 49]]
Model accuracy (for 203 neighbours): 96.65% (+/- 1.16%)
FOLD 1 Number of neighbors 205
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.95 1.00 0.97 76
technology
0.96 0.93 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 205
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.96 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 205
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.93 1.00 0.96 75
technology
0.96 0.89 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 205
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.97 0.99 0.98 75
technology
1.00 0.96 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 1 0]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 205
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 205
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 205
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.94 0.99 0.96 75
technology
0.98 0.89 0.93 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[68 1 1]
[ 1 74 0]
[ 2 4 49]]
Model accuracy (for 205 neighbours): 96.65% (+/- 1.16%)
FOLD 1 Number of neighbors 207
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.95 1.00 0.97 76
technology
0.96 0.93 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 207
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.96 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 207
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.93 1.00 0.96 75
technology
0.96 0.89 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 207
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 207
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 207
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 207
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.94 0.99 0.96 75
technology
0.98 0.89 0.93 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[68 1 1]
[ 1 74 0]
[ 2 4 49]]
Model accuracy (for 207 neighbours): 96.65% (+/- 1.15%)
FOLD 1 Number of neighbors 209
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.95 1.00 0.97 76
technology
0.96 0.93 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 209
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.96 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 209
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.93 1.00 0.96 75
technology
0.96 0.89 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 209
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 209
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
1.00 0.99 0.99 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 209
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 209
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.94 0.99 0.96 75
technology
0.98 0.89 0.93 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[68 1 1]
[ 1 74 0]
[ 2 4 49]]
Model accuracy (for 209 neighbours): 96.65% (+/- 1.15%)
FOLD 1 Number of neighbors 211
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.95 1.00 0.97 76
technology
0.96 0.93 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 211
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.96 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 211
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.93 1.00 0.96 75
technology
0.96 0.89 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 211
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 211
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
1.00 0.99 0.99 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 211
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 211
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.94 0.99 0.96 75
technology
0.98 0.89 0.93 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[68 1 1]
[ 1 74 0]
[ 2 4 49]]
Model accuracy (for 211 neighbours): 96.65% (+/- 1.15%)
FOLD 1 Number of neighbors 213
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.95 1.00 0.97 76
technology
0.96 0.93 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 213
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.96 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 213
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.93 1.00 0.96 75
technology
0.96 0.89 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 213
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 213
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
1.00 0.99 0.99 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 213
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 213
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.95 0.99 0.97 75
technology
0.98 0.89 0.93 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[69 0 1]
[ 1 74 0]
[ 2 4 49]]
Model accuracy (for 213 neighbours): 96.64% (+/- 1.15%)
FOLD 1 Number of neighbors 215
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.95 1.00 0.97 76
technology
0.96 0.93 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 215
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.96 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 215
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.93 1.00 0.96 75
technology
0.96 0.89 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 215
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 215
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
1.00 0.99 0.99 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 215
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 215
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.95 0.99 0.97 75
technology
0.98 0.89 0.93 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[69 0 1]
[ 1 74 0]
[ 2 4 49]]
Model accuracy (for 215 neighbours): 96.64% (+/- 1.16%)
FOLD 1 Number of neighbors 217
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.95 1.00 0.97 76
technology
0.96 0.93 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 217
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.96 0.97 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 1 1]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 217
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.93 1.00 0.96 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.94 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 217
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 217
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
1.00 0.99 0.99 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 217
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 217
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.95 0.99 0.97 75
technology
0.98 0.89 0.93 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[69 0 1]
[ 1 74 0]
[ 2 4 49]]
Model accuracy (for 217 neighbours): 96.64% (+/- 1.16%)
FOLD 1 Number of neighbors 219
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.95 1.00 0.97 76
technology
0.96 0.93 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 219
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.96 0.97 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 1 1]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 219
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.93 1.00 0.96 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.94 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 219
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 219
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
1.00 0.99 0.99 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 219
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 219
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.96 0.99 0.97 75
technology
0.98 0.91 0.94 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[69 0 1]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 219 neighbours): 96.64% (+/- 1.16%)
FOLD 1 Number of neighbors 221
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.95 1.00 0.97 76
technology
0.96 0.93 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 221
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.96 0.97 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 1 1]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 221
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.93 1.00 0.96 75
technology
0.96 0.89 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 3 ):
[[66 2 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 221
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.99 0.99 0.99 75
technology
1.00 0.96 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 221
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
1.00 0.99 0.99 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 221
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 221
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.95 0.99 0.97 75
technology
0.98 0.89 0.93 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[69 0 1]
[ 1 74 0]
[ 2 4 49]]
Model accuracy (for 221 neighbours): 96.64% (+/- 1.16%)
FOLD 1 Number of neighbors 223
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.95 1.00 0.97 76
technology
0.96 0.93 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 223
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.96 0.97 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 1 1]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 223
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 223
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.99 0.99 0.99 75
technology
1.00 0.96 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 223
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
1.00 0.99 0.99 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 223
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 223
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.96 0.99 0.97 75
technology
0.98 0.91 0.94 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[69 0 1]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 223 neighbours): 96.64% (+/- 1.16%)
FOLD 1 Number of neighbors 225
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.95 1.00 0.97 76
technology
0.96 0.93 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 225
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.96 0.97 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 1 1]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 225
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 225
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.99 0.99 0.99 75
technology
1.00 0.96 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 225
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
1.00 0.99 0.99 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 225
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 225
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.96 0.99 0.97 75
technology
0.98 0.93 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[69 0 1]
[ 1 74 0]
[ 1 3 51]]
Model accuracy (for 225 neighbours): 96.64% (+/- 1.16%)
FOLD 1 Number of neighbors 227
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.95 1.00 0.97 76
technology
0.96 0.93 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 227
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.96 0.97 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 1 1]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 227
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 227
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 227
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
1.00 0.99 0.99 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 227
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 227
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.96 0.99 0.97 75
technology
0.98 0.93 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[69 0 1]
[ 1 74 0]
[ 1 3 51]]
Model accuracy (for 227 neighbours): 96.64% (+/- 1.16%)
FOLD 1 Number of neighbors 229
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.95 1.00 0.97 76
technology
0.96 0.93 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 229
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.97 0.97 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 229
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 229
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 229
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
1.00 0.99 0.99 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 229
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 229
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.96 0.99 0.97 75
technology
0.98 0.91 0.94 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[69 0 1]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 229 neighbours): 96.64% (+/- 1.16%)
FOLD 1 Number of neighbors 231
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.95 1.00 0.97 76
technology
0.96 0.93 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 231
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.97 0.97 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 231
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 231
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 231
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
1.00 0.99 0.99 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 231
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 231
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.96 0.99 0.97 75
technology
0.98 0.91 0.94 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[69 0 1]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 231 neighbours): 96.64% (+/- 1.16%)
FOLD 1 Number of neighbors 233
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.95 1.00 0.97 76
technology
0.96 0.93 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 233
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.97 0.97 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 233
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 233
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 233
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
1.00 0.99 0.99 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 233
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 233
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.96 0.99 0.97 75
technology
0.98 0.91 0.94 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[69 0 1]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 233 neighbours): 96.64% (+/- 1.16%)
FOLD 1 Number of neighbors 235
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.95 1.00 0.97 76
technology
0.96 0.93 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 235
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.97 0.97 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 235
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 235
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.99 0.99 0.99 75
technology
1.00 0.96 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 235
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
1.00 0.99 0.99 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 235
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 235
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.96 0.99 0.97 75
technology
1.00 0.91 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 235 neighbours): 96.65% (+/- 1.16%)
FOLD 1 Number of neighbors 237
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.95 1.00 0.97 76
technology
0.96 0.93 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 237
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.97 0.97 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 237
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 237
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 237
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
1.00 0.99 0.99 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 237
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 237
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.96 0.99 0.97 75
technology
1.00 0.91 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 237 neighbours): 96.65% (+/- 1.15%)
FOLD 1 Number of neighbors 239
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.95 1.00 0.97 76
technology
0.96 0.93 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 239
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.97 0.97 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 239
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 239
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.99 0.99 0.99 75
technology
1.00 0.96 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 239
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
1.00 0.99 0.99 75
technology
0.98 0.95 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 0 53]]
FOLD 6 Number of neighbors 239
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 239
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.96 0.99 0.97 75
technology
1.00 0.91 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 239 neighbours): 96.65% (+/- 1.15%)
FOLD 1 Number of neighbors 241
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.95 1.00 0.97 76
technology
0.96 0.93 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 241
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.97 0.97 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 241
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 241
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 241
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
1.00 0.99 0.99 75
technology
0.98 0.95 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 0 53]]
FOLD 6 Number of neighbors 241
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 241
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.95 0.99 0.97 75
technology
1.00 0.89 0.94 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 2 4 49]]
Model accuracy (for 241 neighbours): 96.65% (+/- 1.15%)
FOLD 1 Number of neighbors 243
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.95 1.00 0.97 76
technology
0.96 0.93 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 243
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.97 0.97 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 243
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 243
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 243
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
1.00 0.99 0.99 75
technology
0.98 0.95 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 0 53]]
FOLD 6 Number of neighbors 243
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 243
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.96 0.99 0.97 75
technology
1.00 0.91 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 243 neighbours): 96.65% (+/- 1.15%)
FOLD 1 Number of neighbors 245
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.95 1.00 0.97 76
technology
0.96 0.93 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 245
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.97 0.97 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 245
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 245
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 245
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
1.00 0.99 0.99 75
technology
0.98 0.95 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 0 53]]
FOLD 6 Number of neighbors 245
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 245
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.96 0.99 0.97 75
technology
1.00 0.91 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 245 neighbours): 96.65% (+/- 1.15%)
FOLD 1 Number of neighbors 247
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.95 1.00 0.97 76
technology
0.96 0.93 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 247
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.97 0.96 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 247
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 247
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 247
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
1.00 0.99 0.99 75
technology
0.98 0.95 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 0 53]]
FOLD 6 Number of neighbors 247
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 247
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.95 1.00 0.97 70
sport
0.96 0.99 0.97 75
technology
1.00 0.89 0.94 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 3 3 49]]
Model accuracy (for 247 neighbours): 96.65% (+/- 1.15%)
FOLD 1 Number of neighbors 249
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.95 1.00 0.97 76
technology
0.96 0.93 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 249
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.97 0.96 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 249
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 249
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 249
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
1.00 0.99 0.99 75
technology
0.98 0.95 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 0 53]]
FOLD 6 Number of neighbors 249
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 249
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.96 0.99 0.97 75
technology
1.00 0.91 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 249 neighbours): 96.65% (+/- 1.15%)
FOLD 1 Number of neighbors 251
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 251
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.97 0.96 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 251
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 251
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 251
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 251
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 251
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.96 0.99 0.97 75
technology
1.00 0.91 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 251 neighbours): 96.65% (+/- 1.15%)
FOLD 1 Number of neighbors 253
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 253
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.97 0.96 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 253
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 253
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 253
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 253
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 253
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.96 0.99 0.97 75
technology
1.00 0.91 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 253 neighbours): 96.65% (+/- 1.14%)
FOLD 1 Number of neighbors 255
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 255
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.97 0.96 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 255
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 255
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 255
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 255
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 255
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.96 0.99 0.97 75
technology
1.00 0.91 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 255 neighbours): 96.65% (+/- 1.14%)
FOLD 1 Number of neighbors 257
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 257
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.97 0.96 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 257
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 257
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 257
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 257
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 257
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.96 0.99 0.97 75
technology
1.00 0.91 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 257 neighbours): 96.65% (+/- 1.14%)
FOLD 1 Number of neighbors 259
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 259
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.97 0.96 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 259
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 259
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 259
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 259
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 259
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.95 1.00 0.97 70
sport
0.96 0.99 0.97 75
technology
1.00 0.89 0.94 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 3 3 49]]
Model accuracy (for 259 neighbours): 96.65% (+/- 1.14%)
FOLD 1 Number of neighbors 261
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 261
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.97 0.96 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 261
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 261
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 261
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 261
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.94 1.00 0.97 75
technology
0.94 0.89 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 261
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.95 1.00 0.97 70
sport
0.96 0.99 0.97 75
technology
1.00 0.89 0.94 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 3 3 49]]
Model accuracy (for 261 neighbours): 96.65% (+/- 1.14%)
FOLD 1 Number of neighbors 263
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 263
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.97 0.96 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 263
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 263
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 263
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 263
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 263
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.95 1.00 0.97 70
sport
0.96 0.99 0.97 75
technology
1.00 0.89 0.94 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 3 3 49]]
Model accuracy (for 263 neighbours): 96.65% (+/- 1.14%)
FOLD 1 Number of neighbors 265
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 265
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.97 0.96 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 265
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 265
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 265
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 265
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 265
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.95 1.00 0.97 70
sport
0.96 0.99 0.97 75
technology
1.00 0.89 0.94 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 3 3 49]]
Model accuracy (for 265 neighbours): 96.65% (+/- 1.14%)
FOLD 1 Number of neighbors 267
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 267
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.97 0.96 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 267
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 267
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 267
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 267
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 267
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.95 1.00 0.97 70
sport
0.96 0.99 0.97 75
technology
1.00 0.89 0.94 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 3 3 49]]
Model accuracy (for 267 neighbours): 96.65% (+/- 1.13%)
FOLD 1 Number of neighbors 269
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 269
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.97 0.96 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 269
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 269
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 269
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 269
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 269
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.96 0.99 0.97 75
technology
1.00 0.91 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 269 neighbours): 96.65% (+/- 1.13%)
FOLD 1 Number of neighbors 271
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 271
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.97 0.96 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 271
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 271
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 271
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 271
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 271
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.96 0.99 0.97 75
technology
1.00 0.91 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 2 3 50]]
Model accuracy (for 271 neighbours): 96.65% (+/- 1.13%)
FOLD 1 Number of neighbors 273
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 273
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.97 0.96 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 273
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 273
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 273
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 273
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 273
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.95 1.00 0.97 70
sport
0.96 0.99 0.97 75
technology
1.00 0.89 0.94 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 3 3 49]]
Model accuracy (for 273 neighbours): 96.65% (+/- 1.13%)
FOLD 1 Number of neighbors 275
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 275
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.97 0.96 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 275
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 275
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 275
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 275
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 275
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.95 1.00 0.97 70
sport
0.96 0.99 0.97 75
technology
1.00 0.89 0.94 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 3 3 49]]
Model accuracy (for 275 neighbours): 96.65% (+/- 1.13%)
FOLD 1 Number of neighbors 277
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.95 1.00 0.97 76
technology
0.96 0.93 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 277
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.97 0.96 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 277
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 277
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 277
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 277
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 277
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.95 1.00 0.97 70
sport
0.96 0.99 0.97 75
technology
1.00 0.89 0.94 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 3 3 49]]
Model accuracy (for 277 neighbours): 96.65% (+/- 1.13%)
FOLD 1 Number of neighbors 279
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 279
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.97 0.96 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 279
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 279
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 279
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 279
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 279
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.95 1.00 0.97 70
sport
0.96 0.99 0.97 75
technology
1.00 0.89 0.94 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 3 3 49]]
Model accuracy (for 279 neighbours): 96.65% (+/- 1.12%)
FOLD 1 Number of neighbors 281
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 281
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.97 0.96 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 281
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 281
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 281
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 281
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 281
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.95 1.00 0.97 70
sport
0.96 0.99 0.97 75
technology
1.00 0.89 0.94 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 3 3 49]]
Model accuracy (for 281 neighbours): 96.65% (+/- 1.12%)
FOLD 1 Number of neighbors 283
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 283
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.97 0.96 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 283
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 283
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 283
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 283
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 283
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.95 1.00 0.97 70
sport
0.96 0.99 0.97 75
technology
1.00 0.89 0.94 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 3 3 49]]
Model accuracy (for 283 neighbours): 96.65% (+/- 1.12%)
FOLD 1 Number of neighbors 285
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 285
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.97 0.96 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 285
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 285
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 285
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 285
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 285
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.95 1.00 0.97 70
sport
0.96 0.99 0.97 75
technology
1.00 0.89 0.94 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 3 3 49]]
Model accuracy (for 285 neighbours): 96.65% (+/- 1.12%)
FOLD 1 Number of neighbors 287
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 287
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.97 0.96 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 287
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 287
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 287
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 287
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 287
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.95 1.00 0.97 70
sport
0.96 0.99 0.97 75
technology
1.00 0.89 0.94 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 3 3 49]]
Model accuracy (for 287 neighbours): 96.66% (+/- 1.12%)
FOLD 1 Number of neighbors 289
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 289
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.97 0.96 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 289
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 289
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.97 0.99 0.98 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 289
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.93 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 3 1 52]]
FOLD 6 Number of neighbors 289
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 289
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.95 1.00 0.97 70
sport
0.96 0.99 0.97 75
technology
1.00 0.89 0.94 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 3 3 49]]
Model accuracy (for 289 neighbours): 96.66% (+/- 1.11%)
FOLD 1 Number of neighbors 291
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 291
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.97 0.96 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 291
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 291
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.97 0.97 0.97 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 2 73 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 291
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.95 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 2 1 53]]
FOLD 6 Number of neighbors 291
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 291
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.95 1.00 0.97 70
sport
0.96 0.99 0.97 75
technology
1.00 0.89 0.94 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 3 3 49]]
Model accuracy (for 291 neighbours): 96.66% (+/- 1.11%)
FOLD 1 Number of neighbors 293
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 293
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.97 0.96 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 293
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 293
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.97 0.97 0.97 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 2 73 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 293
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.95 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 2 1 53]]
FOLD 6 Number of neighbors 293
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 293
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.95 1.00 0.97 70
sport
0.95 0.99 0.97 75
technology
1.00 0.87 0.93 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 3 4 48]]
Model accuracy (for 293 neighbours): 96.66% (+/- 1.11%)
FOLD 1 Number of neighbors 295
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 295
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.97 0.96 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 295
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 295
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.97 0.97 0.97 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 2 73 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 295
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.95 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 2 1 53]]
FOLD 6 Number of neighbors 295
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 295
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.93 1.00 0.97 70
sport
0.95 0.99 0.97 75
technology
1.00 0.85 0.92 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 4 4 47]]
Model accuracy (for 295 neighbours): 96.66% (+/- 1.11%)
FOLD 1 Number of neighbors 297
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 297
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.97 0.96 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 297
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 297
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.97 0.97 0.97 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 2 73 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 297
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.95 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 2 1 53]]
FOLD 6 Number of neighbors 297
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 297
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.93 1.00 0.97 70
sport
0.95 0.99 0.97 75
technology
1.00 0.85 0.92 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 4 4 47]]
Model accuracy (for 297 neighbours): 96.65% (+/- 1.11%)
FOLD 1 Number of neighbors 299
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
0.95 1.00 0.97 76
technology
0.98 0.93 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 299
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.97 0.96 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 299
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.94 1.00 0.97 75
technology
0.98 0.89 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 2 4 50]]
FOLD 4 Number of neighbors 299
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 1.00 0.98 70
sport
0.97 0.97 0.97 75
technology
1.00 0.95 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 2 73 0]
[ 1 2 53]]
FOLD 5 Number of neighbors 299
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.99 0.99 0.99 75
technology
0.98 0.95 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 2 1 53]]
FOLD 6 Number of neighbors 299
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.94 1.00 0.97 75
technology
0.96 0.89 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[67 1 2]
[ 0 75 0]
[ 2 4 50]]
FOLD 7 Number of neighbors 299
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.93 1.00 0.97 70
sport
0.95 0.99 0.97 75
technology
1.00 0.85 0.92 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[70 0 0]
[ 1 74 0]
[ 4 4 47]]
Model accuracy (for 299 neighbours): 96.65% (+/- 1.11%)
In [378]:
# changing to misclassification error
MSE_ngram_not_bal = [1-x/100 for x in k_model_accuracy_ngram_not_bal]
index_ngram_not_bal=MSE_ngram_not_bal.index(min(MSE_ngram_not_bal))
optimal_k_ngram_not_bal = neighbors[index_ngram_not_bal]
print ("The highest model accuracy",k_model_accuracy_ngram_not_bal[index_ngram_not_bal],"is achieved by using optimal number of neighbors %d" % optimal_k_ngram_not_bal)
# plot misclassification error vs k
plt.plot(neighbors, MSE_not_bal)
plt.xlabel('Number of Neighbors K')
plt.ylabel('Misclassification Error')
plt.show()
The highest model accuracy 96.80225321345199 is achieved by using optimal number of neighbors 125
In [379]:
cvscores = []
k_model_accuracy=[]
kfold = StratifiedKFold(n_splits=7, shuffle=True, random_state=seed)
for k in neighbors:
fold=0
model = KNeighborsClassifier(n_neighbors=k, metric='cosine')
for train, test in kfold.split(X_term_weighting, class_labels):
fold+=1
print('FOLD',fold, 'Number of neighbors', k)
labels_train=[]
for i in range(len(train)):
labels_train.append(class_labels[train[i]])
labels_test=[]
for i in range(len(test)):
labels_test.append(class_labels[test[i]])
# Plot a bar plot of the labels: class distribution is adjusted
#seaborn.countplot - Show value counts for a single categorical variable:
print('Class Distribution --> Train data (Fold',fold,'):')
ax = sns.countplot(labels_train)
ax.set_title("Distribution of the Labels (without N/A)")
plt.show()
# Apply the random under-sampling
#Oversampling and undersampling in data analysis are techniques used to adjust the class distribution of a data set
#(i.e. the ratio between the different classes/categories represented).
rus = RandomUnderSampler(return_indices=True)
train_rus, train_labels_rus, idx_resampled = rus.fit_sample(X_term_weighting[train], labels_train)
train_rus, train_labels_rus = shuffle(train_rus, train_labels_rus)
# Plot a bar plot of the labels
#seaborn.countplot - Show value counts for a single categorical variable:
print('Class Distribution after performing under-sampling --> Train data (Fold',fold,'):')
ax = sns.countplot(train_labels_rus)
sns.countplot(train_labels_rus) #--> class distribution is adjusted
plt.show()
# Fit/Train the model
model.fit(train_rus, train_labels_rus)
#Evaluate the Model; Use the test dataset to evaluate the model
print('\n\n ****** Test Data ******** (Fold',fold,'):')
predicted = model.predict(X_term_weighting[test])
# Print performance details
print(metrics.classification_report(labels_test, predicted))
# Print confusion matrix
print('Confusion Matrix (Fold',fold,'):')
print(metrics.confusion_matrix(labels_test, predicted))
cvscores.append(accuracy_score(labels_test, predicted) * 100)
print("\n\n Model accuracy (for",k," neighbours): %.2f%% (+/- %.2f%%)" % (numpy.mean(cvscores), numpy.std(cvscores)))
k_model_accuracy.append(numpy.mean(cvscores))
FOLD 1 Number of neighbors 1 Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.94 0.94 0.94 71
sport
1.00 0.96 0.98 76
technology
0.92 0.96 0.94 56
avg / total 0.96 0.96 0.96 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 2 73 1]
[ 2 0 54]]
FOLD 2 Number of neighbors 1
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.89 0.93 70
sport
0.96 0.96 0.96 75
technology
0.87 0.96 0.92 56
avg / total 0.94 0.94 0.94 201
Confusion Matrix (Fold 2 ):
[[62 1 7]
[ 2 72 1]
[ 0 2 54]]
FOLD 3 Number of neighbors 1
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.99 0.96 0.97 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 2 72 1]
[ 0 0 56]]
FOLD 4 Number of neighbors 1
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.93 0.97 0.95 70
sport
0.99 0.93 0.96 75
technology
0.95 0.96 0.96 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 4 ):
[[68 1 1]
[ 3 70 2]
[ 2 0 54]]
FOLD 5 Number of neighbors 1
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
1.00 0.89 0.94 70
sport
0.99 0.97 0.98 75
technology
0.86 1.00 0.93 56
avg / total 0.96 0.95 0.95 201
Confusion Matrix (Fold 5 ):
[[62 1 7]
[ 0 73 2]
[ 0 0 56]]
FOLD 6 Number of neighbors 1
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.94 0.93 0.94 70
sport
0.95 0.97 0.96 75
technology
0.95 0.93 0.94 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[65 3 2]
[ 1 73 1]
[ 3 1 52]]
FOLD 7 Number of neighbors 1
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.90 0.89 0.89 70
sport
0.91 0.96 0.94 75
technology
0.90 0.85 0.88 55
avg / total 0.90 0.91 0.90 200
Confusion Matrix (Fold 7 ):
[[62 4 4]
[ 2 72 1]
[ 5 3 47]]
Model accuracy (for 1 neighbours): 94.60% (+/- 2.01%)
FOLD 1 Number of neighbors 3
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.99 0.97 0.98 76
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 74 1]
[ 1 1 54]]
FOLD 2 Number of neighbors 3
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.89 0.93 70
sport
0.97 0.96 0.97 75
technology
0.87 0.98 0.92 56
avg / total 0.94 0.94 0.94 201
Confusion Matrix (Fold 2 ):
[[62 1 7]
[ 2 72 1]
[ 0 1 55]]
FOLD 3 Number of neighbors 3
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 0.99 0.98 75
technology
0.93 1.00 0.97 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 1 74 0]
[ 0 0 56]]
FOLD 4 Number of neighbors 3
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.99 0.95 0.97 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[68 1 1]
[ 2 71 2]
[ 1 0 55]]
FOLD 5 Number of neighbors 3
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 0.99 0.98 75
technology
0.92 0.98 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 5 ):
[[64 1 5]
[ 1 74 0]
[ 0 1 55]]
FOLD 6 Number of neighbors 3
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.94 0.91 0.93 70
sport
0.97 0.97 0.97 75
technology
0.88 0.91 0.89 56
avg / total 0.94 0.94 0.94 201
Confusion Matrix (Fold 6 ):
[[64 0 6]
[ 1 73 1]
[ 3 2 51]]
FOLD 7 Number of neighbors 3
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.94 0.93 0.94 70
sport
0.97 0.95 0.96 75
technology
0.91 0.96 0.94 55
avg / total 0.95 0.94 0.95 200
Confusion Matrix (Fold 7 ):
[[65 1 4]
[ 3 71 1]
[ 1 1 53]]
Model accuracy (for 3 neighbours): 95.02% (+/- 1.75%)
FOLD 1 Number of neighbors 5
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.94 0.96 71
sport
0.99 0.97 0.98 76
technology
0.92 0.96 0.94 56
avg / total 0.96 0.96 0.96 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 1 74 1]
[ 1 1 54]]
FOLD 2 Number of neighbors 5
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.97 0.97 0.97 75
technology
0.92 0.98 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[65 1 4]
[ 1 73 1]
[ 0 1 55]]
FOLD 3 Number of neighbors 5
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 1.00 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 4 Number of neighbors 5
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
1.00 0.96 0.98 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 72 1]
[ 1 0 55]]
FOLD 5 Number of neighbors 5
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.99 0.99 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[65 1 4]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 5
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.94 0.90 0.92 70
sport
0.96 0.99 0.97 75
technology
0.89 0.91 0.90 56
avg / total 0.94 0.94 0.94 201
Confusion Matrix (Fold 6 ):
[[63 1 6]
[ 1 74 0]
[ 3 2 51]]
FOLD 7 Number of neighbors 5
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.94 0.96 0.95 70
sport
0.97 0.95 0.96 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.95 0.96 200
Confusion Matrix (Fold 7 ):
[[67 1 2]
[ 3 71 1]
[ 1 1 53]]
Model accuracy (for 5 neighbours): 95.36% (+/- 1.65%)
FOLD 1 Number of neighbors 7
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.93 0.95 71
sport
0.99 0.97 0.98 76
technology
0.90 0.96 0.93 56
avg / total 0.96 0.96 0.96 203
Confusion Matrix (Fold 1 ):
[[66 0 5]
[ 1 74 1]
[ 1 1 54]]
FOLD 2 Number of neighbors 7
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
1.00 0.96 0.98 75
technology
0.92 1.00 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 0 4]
[ 2 72 1]
[ 0 0 56]]
FOLD 3 Number of neighbors 7
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 1.00 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 4 Number of neighbors 7
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.99 0.96 0.97 75
technology
0.96 0.98 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[68 1 1]
[ 2 72 1]
[ 1 0 55]]
FOLD 5 Number of neighbors 7
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 0.99 0.99 75
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 0 74 1]
[ 0 0 56]]
FOLD 6 Number of neighbors 7
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.95 0.90 0.93 70
sport
0.96 0.99 0.97 75
technology
0.90 0.93 0.91 56
avg / total 0.94 0.94 0.94 201
Confusion Matrix (Fold 6 ):
[[63 1 6]
[ 1 74 0]
[ 2 2 52]]
FOLD 7 Number of neighbors 7
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 0.99 0.98 75
technology
0.93 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 1 4]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 7 neighbours): 95.58% (+/- 1.58%)
FOLD 1 Number of neighbors 9
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.93 0.96 71
sport
0.99 0.99 0.99 76
technology
0.90 0.96 0.93 56
avg / total 0.96 0.96 0.96 203
Confusion Matrix (Fold 1 ):
[[66 0 5]
[ 0 75 1]
[ 1 1 54]]
FOLD 2 Number of neighbors 9
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
1.00 0.97 0.99 75
technology
0.92 1.00 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 0 4]
[ 1 73 1]
[ 0 0 56]]
FOLD 3 Number of neighbors 9
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
1.00 0.93 0.96 70
sport
0.99 0.99 0.99 75
technology
0.92 1.00 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 74 1]
[ 0 0 56]]
FOLD 4 Number of neighbors 9
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.93 0.96 0.94 70
sport
0.99 0.95 0.97 75
technology
0.95 0.96 0.96 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 3 71 1]
[ 2 0 54]]
FOLD 5 Number of neighbors 9
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.99 0.99 0.99 75
technology
0.92 1.00 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[64 1 5]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 9
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.96 0.91 0.93 70
sport
0.96 0.97 0.97 75
technology
0.90 0.93 0.91 56
avg / total 0.94 0.94 0.94 201
Confusion Matrix (Fold 6 ):
[[64 1 5]
[ 1 73 1]
[ 2 2 52]]
FOLD 7 Number of neighbors 9
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 0.96 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 9 neighbours): 95.69% (+/- 1.50%)
FOLD 1 Number of neighbors 11
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.92 0.94 71
sport
0.99 0.99 0.99 76
technology
0.90 0.96 0.93 56
avg / total 0.96 0.96 0.96 203
Confusion Matrix (Fold 1 ):
[[65 0 6]
[ 1 75 0]
[ 1 1 54]]
FOLD 2 Number of neighbors 11
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 0.97 0.98 75
technology
0.90 0.98 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[65 0 5]
[ 1 73 1]
[ 0 1 55]]
FOLD 3 Number of neighbors 11
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 4 Number of neighbors 11
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.97 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 2 73 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 11
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
1.00 0.99 0.99 75
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[66 0 4]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 11
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.94 0.90 0.92 70
sport
0.96 0.99 0.97 75
technology
0.91 0.93 0.92 56
avg / total 0.94 0.94 0.94 201
Confusion Matrix (Fold 6 ):
[[63 2 5]
[ 1 74 0]
[ 3 1 52]]
FOLD 7 Number of neighbors 11
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.93 0.96 0.95 55
avg / total 0.97 0.96 0.97 200
Confusion Matrix (Fold 7 ):
[[66 0 4]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 11 neighbours): 95.81% (+/- 1.48%)
FOLD 1 Number of neighbors 13
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.96 0.96 71
sport
1.00 0.99 0.99 76
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 1 75 0]
[ 1 0 55]]
FOLD 2 Number of neighbors 13
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 0.97 0.98 75
technology
0.90 0.98 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[65 0 5]
[ 1 73 1]
[ 0 1 55]]
FOLD 3 Number of neighbors 13
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 13
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.96 0.98 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 3 72 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 13
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
1.00 0.99 0.99 75
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[66 0 4]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 13
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.97 0.99 0.98 75
technology
0.91 0.93 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[65 0 5]
[ 1 74 0]
[ 2 2 52]]
FOLD 7 Number of neighbors 13
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.99 0.99 0.99 75
technology
0.93 0.95 0.94 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 0 4]
[ 1 74 0]
[ 2 1 52]]
Model accuracy (for 13 neighbours): 95.93% (+/- 1.44%)
FOLD 1 Number of neighbors 15
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.94 0.96 71
sport
1.00 1.00 1.00 76
technology
0.93 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 1 0 55]]
FOLD 2 Number of neighbors 15
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 0.97 0.98 75
technology
0.90 0.98 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[65 0 5]
[ 1 73 1]
[ 0 1 55]]
FOLD 3 Number of neighbors 15
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 15
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
1.00 0.97 0.99 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 15
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.97 0.99 0.98 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[65 1 4]
[ 1 74 0]
[ 0 1 55]]
FOLD 6 Number of neighbors 15
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.95 0.90 0.93 70
sport
0.95 0.99 0.97 75
technology
0.91 0.93 0.92 56
avg / total 0.94 0.94 0.94 201
Confusion Matrix (Fold 6 ):
[[63 2 5]
[ 1 74 0]
[ 2 2 52]]
FOLD 7 Number of neighbors 15
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.99 0.97 0.98 75
technology
0.93 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 0 4]
[ 2 73 0]
[ 1 1 53]]
Model accuracy (for 15 neighbours): 96.00% (+/- 1.43%)
FOLD 1 Number of neighbors 17
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.93 0.96 71
sport
1.00 1.00 1.00 76
technology
0.92 0.98 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[66 0 5]
[ 0 76 0]
[ 1 0 55]]
FOLD 2 Number of neighbors 17
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 0.97 0.98 75
technology
0.90 0.98 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[65 0 5]
[ 1 73 1]
[ 0 1 55]]
FOLD 3 Number of neighbors 17
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 17
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.97 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 2 73 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 17
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.99 0.99 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[65 1 4]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 17
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.94 0.91 0.93 70
sport
0.97 0.99 0.98 75
technology
0.91 0.93 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[64 1 5]
[ 1 74 0]
[ 3 1 52]]
FOLD 7 Number of neighbors 17
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.97 0.99 0.98 75
technology
0.93 0.98 0.96 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 1 4]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 17 neighbours): 96.05% (+/- 1.39%)
FOLD 1 Number of neighbors 19
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 19
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 0.97 0.98 75
technology
0.90 0.98 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[65 0 5]
[ 1 73 1]
[ 0 1 55]]
FOLD 3 Number of neighbors 19
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 19
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.97 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 2 73 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 19
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 0.99 0.99 75
technology
0.93 1.00 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[65 1 4]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 19
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.89 0.93 70
sport
0.94 1.00 0.97 75
technology
0.91 0.93 0.92 56
avg / total 0.94 0.94 0.94 201
Confusion Matrix (Fold 6 ):
[[62 3 5]
[ 0 75 0]
[ 2 2 52]]
FOLD 7 Number of neighbors 19
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.93 0.98 0.96 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[66 0 4]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 19 neighbours): 96.11% (+/- 1.38%)
FOLD 1 Number of neighbors 21
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.94 0.96 71
sport
1.00 1.00 1.00 76
technology
0.93 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 1 0 55]]
FOLD 2 Number of neighbors 21
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.99 0.97 0.98 75
technology
0.90 1.00 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[64 1 5]
[ 1 73 1]
[ 0 0 56]]
FOLD 3 Number of neighbors 21
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 21
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.97 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 21
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 0.99 0.99 75
technology
0.93 1.00 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[65 1 4]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 21
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.98 0.90 0.94 70
sport
0.95 1.00 0.97 75
technology
0.91 0.95 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[63 2 5]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 21
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.93 0.98 0.96 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[66 0 4]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 21 neighbours): 96.17% (+/- 1.36%)
FOLD 1 Number of neighbors 23
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
1.00 1.00 1.00 76
technology
0.95 1.00 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 23
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.97 0.98 75
technology
0.93 1.00 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 1 3]
[ 1 73 1]
[ 0 0 56]]
FOLD 3 Number of neighbors 23
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 23
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.97 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 2 73 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 23
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
1.00 0.99 0.99 75
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[66 0 4]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 23
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.98 0.87 0.92 70
sport
0.95 1.00 0.97 75
technology
0.90 0.96 0.93 56
avg / total 0.95 0.95 0.94 201
Confusion Matrix (Fold 6 ):
[[61 3 6]
[ 0 75 0]
[ 1 1 54]]
FOLD 7 Number of neighbors 23
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.97 0.99 0.98 75
technology
0.93 0.98 0.96 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 1 4]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 23 neighbours): 96.23% (+/- 1.36%)
FOLD 1 Number of neighbors 25
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 25
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.97 0.98 75
technology
0.93 1.00 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 1 3]
[ 1 73 1]
[ 0 0 56]]
FOLD 3 Number of neighbors 25
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 25
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 25
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 0.99 0.99 75
technology
0.93 1.00 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[65 1 4]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 25
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.95 0.89 0.92 70
sport
0.96 1.00 0.98 75
technology
0.90 0.93 0.91 56
avg / total 0.94 0.94 0.94 201
Confusion Matrix (Fold 6 ):
[[62 2 6]
[ 0 75 0]
[ 3 1 52]]
FOLD 7 Number of neighbors 25
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.93 0.96 0.95 55
avg / total 0.97 0.96 0.97 200
Confusion Matrix (Fold 7 ):
[[66 0 4]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 25 neighbours): 96.27% (+/- 1.36%)
FOLD 1 Number of neighbors 27
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 27
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.97 0.98 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 73 1]
[ 0 0 56]]
FOLD 3 Number of neighbors 27
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 27
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.97 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 73 1]
[ 0 0 56]]
FOLD 5 Number of neighbors 27
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 27
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.89 0.93 70
sport
0.95 1.00 0.97 75
technology
0.91 0.95 0.93 56
avg / total 0.95 0.95 0.94 201
Confusion Matrix (Fold 6 ):
[[62 3 5]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 27
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 0.96 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 27 neighbours): 96.33% (+/- 1.36%)
FOLD 1 Number of neighbors 29
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 29
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.97 0.98 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 73 1]
[ 0 0 56]]
FOLD 3 Number of neighbors 29
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 29
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.96 0.98 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 2 72 1]
[ 1 0 55]]
FOLD 5 Number of neighbors 29
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 29
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.90 0.93 70
sport
0.96 1.00 0.98 75
technology
0.91 0.95 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[63 2 5]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 29
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 0.96 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 29 neighbours): 96.37% (+/- 1.34%)
FOLD 1 Number of neighbors 31
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 31
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 0.97 0.97 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 73 1]
[ 0 1 55]]
FOLD 3 Number of neighbors 31
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 31
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 31
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 31
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.90 0.93 70
sport
0.96 1.00 0.98 75
technology
0.91 0.95 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[63 2 5]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 31
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.96 0.95 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 31 neighbours): 96.40% (+/- 1.33%)
FOLD 1 Number of neighbors 33
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 33
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.97 0.98 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 73 1]
[ 0 0 56]]
FOLD 3 Number of neighbors 33
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 33
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.97 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 73 1]
[ 1 0 55]]
FOLD 5 Number of neighbors 33
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.99 0.97 0.98 75
technology
0.95 1.00 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 2 73 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 33
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.95 0.89 0.92 70
sport
0.95 1.00 0.97 75
technology
0.91 0.93 0.92 56
avg / total 0.94 0.94 0.94 201
Confusion Matrix (Fold 6 ):
[[62 3 5]
[ 0 75 0]
[ 3 1 52]]
FOLD 7 Number of neighbors 33
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 33 neighbours): 96.42% (+/- 1.33%)
FOLD 1 Number of neighbors 35
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 35
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.96 0.97 75
technology
0.95 1.00 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 2 72 1]
[ 0 0 56]]
FOLD 3 Number of neighbors 35
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 35
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 35
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 35
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.97 1.00 0.99 75
technology
0.91 0.95 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[64 1 5]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 35
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 35 neighbours): 96.46% (+/- 1.32%)
FOLD 1 Number of neighbors 37
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 37
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.97 0.98 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 73 1]
[ 0 0 56]]
FOLD 3 Number of neighbors 37
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 37
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.97 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 73 1]
[ 0 0 56]]
FOLD 5 Number of neighbors 37
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 37
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.89 0.93 70
sport
0.95 1.00 0.97 75
technology
0.91 0.95 0.93 56
avg / total 0.95 0.95 0.94 201
Confusion Matrix (Fold 6 ):
[[62 3 5]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 37
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 37 neighbours): 96.48% (+/- 1.31%)
FOLD 1 Number of neighbors 39
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 39
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.97 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 1 73 1]
[ 0 0 56]]
FOLD 3 Number of neighbors 39
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 39
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 39
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.99 0.97 0.98 75
technology
0.95 1.00 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 2 73 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 39
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.90 0.93 70
sport
0.96 1.00 0.98 75
technology
0.91 0.95 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[63 2 5]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 39
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 39 neighbours): 96.51% (+/- 1.31%)
FOLD 1 Number of neighbors 41
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 41
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.96 0.97 75
technology
0.95 1.00 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 2 72 1]
[ 0 0 56]]
FOLD 3 Number of neighbors 41
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 41
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 41
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 41
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 41
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 41 neighbours): 96.54% (+/- 1.30%)
FOLD 1 Number of neighbors 43
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 43
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 0.96 0.97 75
technology
0.93 1.00 0.97 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[65 2 3]
[ 2 72 1]
[ 0 0 56]]
FOLD 3 Number of neighbors 43
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 43
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 43
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 43
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 43
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 43 neighbours): 96.56% (+/- 1.30%)
FOLD 1 Number of neighbors 45
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 45
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.97 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 1 73 1]
[ 0 0 56]]
FOLD 3 Number of neighbors 45
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 45
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 45
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 45
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.97 1.00 0.99 75
technology
0.91 0.95 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[64 1 5]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 45
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 45 neighbours): 96.59% (+/- 1.29%)
FOLD 1 Number of neighbors 47
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 47
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.97 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 1 73 1]
[ 0 0 56]]
FOLD 3 Number of neighbors 47
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 47
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 47
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 47
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.97 1.00 0.99 75
technology
0.91 0.95 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[64 1 5]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 47
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 0.96 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 47 neighbours): 96.62% (+/- 1.28%)
FOLD 1 Number of neighbors 49
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 49
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.97 0.98 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 2 73 0]
[ 0 0 56]]
FOLD 3 Number of neighbors 49
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 49
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 49
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 49
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.90 0.93 70
sport
0.96 1.00 0.98 75
technology
0.91 0.95 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[63 2 5]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 49
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 49 neighbours): 96.64% (+/- 1.28%)
FOLD 1 Number of neighbors 51
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 51
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 0.96 0.97 75
technology
0.95 1.00 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 72 1]
[ 0 0 56]]
FOLD 3 Number of neighbors 51
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 51
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 51
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 51
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 51
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 51 neighbours): 96.66% (+/- 1.27%)
FOLD 1 Number of neighbors 53
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 53
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.97 0.98 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 73 1]
[ 0 0 56]]
FOLD 3 Number of neighbors 53
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 53
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 53
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 53
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.99 1.00 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 0 54]]
FOLD 7 Number of neighbors 53
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 53 neighbours): 96.68% (+/- 1.26%)
FOLD 1 Number of neighbors 55
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 55
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.97 0.98 75
technology
0.93 1.00 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 1 3]
[ 1 73 1]
[ 0 0 56]]
FOLD 3 Number of neighbors 55
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 55
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 55
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 55
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 55
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 55 neighbours): 96.70% (+/- 1.24%)
FOLD 1 Number of neighbors 57
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 57
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.99 0.96 0.97 75
technology
0.93 1.00 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 1 3]
[ 2 72 1]
[ 0 0 56]]
FOLD 3 Number of neighbors 57
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 57
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 57
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 57
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 57
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.96 0.99 0.97 75
technology
0.95 0.98 0.96 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 57 neighbours): 96.71% (+/- 1.23%)
FOLD 1 Number of neighbors 59
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
1.00 1.00 1.00 76
technology
0.95 1.00 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 59
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 0.97 0.97 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 73 1]
[ 0 1 55]]
FOLD 3 Number of neighbors 59
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 59
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 59
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 59
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.97 1.00 0.99 75
technology
0.93 0.96 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[65 1 4]
[ 0 75 0]
[ 1 1 54]]
FOLD 7 Number of neighbors 59
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.96 0.99 0.97 75
technology
0.95 0.98 0.96 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 59 neighbours): 96.74% (+/- 1.23%)
FOLD 1 Number of neighbors 61
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 61
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.99 0.97 0.98 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 2 73 0]
[ 0 1 55]]
FOLD 3 Number of neighbors 61
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 61
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 61
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
1.00 0.96 0.98 70
sport
1.00 1.00 1.00 75
technology
0.95 1.00 0.97 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 61
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 61
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.98 0.96 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 61 neighbours): 96.76% (+/- 1.22%)
FOLD 1 Number of neighbors 63
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 63
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 3 Number of neighbors 63
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 63
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 63
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 63
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.99 1.00 0.99 75
technology
0.93 0.96 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[65 1 4]
[ 0 75 0]
[ 2 0 54]]
FOLD 7 Number of neighbors 63
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.96 0.99 0.97 75
technology
0.93 0.96 0.95 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[64 2 4]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 63 neighbours): 96.77% (+/- 1.21%)
FOLD 1 Number of neighbors 65
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 65
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.96 0.99 0.97 75
technology
0.96 0.98 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 0 1 55]]
FOLD 3 Number of neighbors 65
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 65
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 65
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
1.00 0.96 0.98 70
sport
1.00 1.00 1.00 75
technology
0.95 1.00 0.97 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 65
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.92 0.96 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[64 1 5]
[ 0 75 0]
[ 1 1 54]]
FOLD 7 Number of neighbors 65
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 65 neighbours): 96.79% (+/- 1.21%)
FOLD 1 Number of neighbors 67
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
0.99 1.00 0.99 76
technology
0.93 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 67
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 0.97 0.97 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 73 1]
[ 0 1 55]]
FOLD 3 Number of neighbors 67
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 67
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 67
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 67
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.97 1.00 0.99 75
technology
0.93 0.96 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[65 1 4]
[ 0 75 0]
[ 1 1 54]]
FOLD 7 Number of neighbors 67
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.96 0.99 0.97 75
technology
0.93 0.98 0.96 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[64 2 4]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 67 neighbours): 96.80% (+/- 1.20%)
FOLD 1 Number of neighbors 69
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
0.99 1.00 0.99 76
technology
0.93 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 69
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.99 0.96 0.97 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 2 72 1]
[ 0 1 55]]
FOLD 3 Number of neighbors 69
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 69
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 69
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 69
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 69
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 69 neighbours): 96.80% (+/- 1.19%)
FOLD 1 Number of neighbors 71
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 71
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.97 0.97 75
technology
0.96 0.98 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 2 73 0]
[ 0 1 55]]
FOLD 3 Number of neighbors 71
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 71
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 71
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 71
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.97 1.00 0.99 75
technology
0.93 0.96 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[65 1 4]
[ 0 75 0]
[ 1 1 54]]
FOLD 7 Number of neighbors 71
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.96 0.99 0.97 75
technology
0.95 0.98 0.96 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 71 neighbours): 96.82% (+/- 1.18%)
FOLD 1 Number of neighbors 73
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
0.99 1.00 0.99 76
technology
0.93 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 73
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.99 0.97 0.98 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 2 73 0]
[ 0 1 55]]
FOLD 3 Number of neighbors 73
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 73
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 73
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 73
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.92 0.96 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[64 1 5]
[ 0 75 0]
[ 1 1 54]]
FOLD 7 Number of neighbors 73
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.96 0.99 0.97 75
technology
0.95 0.98 0.96 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 73 neighbours): 96.83% (+/- 1.17%)
FOLD 1 Number of neighbors 75
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 75
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 75
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 75
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 75
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 75
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 75
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 75 neighbours): 96.84% (+/- 1.16%)
FOLD 1 Number of neighbors 77
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
0.99 1.00 0.99 76
technology
0.93 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 77
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 77
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 1.00 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 77
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 77
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 77
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 77
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.96 0.99 0.97 75
technology
0.95 0.98 0.96 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 77 neighbours): 96.84% (+/- 1.16%)
FOLD 1 Number of neighbors 79
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
0.99 1.00 0.99 76
technology
0.93 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 79
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 79
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 79
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 79
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 79
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.93 0.96 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[65 1 4]
[ 0 75 0]
[ 0 1 55]]
FOLD 7 Number of neighbors 79
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.96 0.99 0.97 75
technology
0.95 0.98 0.96 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 79 neighbours): 96.85% (+/- 1.15%)
FOLD 1 Number of neighbors 81
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
0.99 1.00 0.99 76
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 81
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.94 0.94 70
sport
0.95 0.95 0.95 75
technology
0.95 0.95 0.95 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 3 71 1]
[ 1 2 53]]
FOLD 3 Number of neighbors 81
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 81
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 81
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 81
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 1 55]]
FOLD 7 Number of neighbors 81
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.96 0.99 0.97 75
technology
0.93 0.96 0.95 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[64 2 4]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 81 neighbours): 96.85% (+/- 1.15%)
FOLD 1 Number of neighbors 83
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
0.99 1.00 0.99 76
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 83
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.96 0.97 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 2 73 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 83
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.97 1.00 0.99 75
technology
0.93 0.96 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 83
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 83
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 83
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 1 55]]
FOLD 7 Number of neighbors 83
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.99 0.99 75
technology
0.95 0.98 0.96 55
avg / total 0.98 0.97 0.98 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 83 neighbours): 96.87% (+/- 1.15%)
FOLD 1 Number of neighbors 85
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
0.99 1.00 0.99 76
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 85
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.96 0.95 70
sport
0.96 0.96 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 85
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 85
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 85
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 85
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.93 0.96 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[65 1 4]
[ 0 75 0]
[ 0 1 55]]
FOLD 7 Number of neighbors 85
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.96 0.99 0.97 75
technology
0.95 0.98 0.96 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 85 neighbours): 96.88% (+/- 1.14%)
FOLD 1 Number of neighbors 87
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
0.99 1.00 0.99 76
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 87
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 0.97 0.97 75
technology
0.96 0.98 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 0 1 55]]
FOLD 3 Number of neighbors 87
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.99 1.00 0.99 75
technology
0.93 0.96 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 87
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.97 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 87
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 87
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 1 55]]
FOLD 7 Number of neighbors 87
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.96 0.99 0.97 75
technology
0.93 0.98 0.96 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[64 2 4]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 87 neighbours): 96.88% (+/- 1.14%)
FOLD 1 Number of neighbors 89
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
0.99 1.00 0.99 76
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 89
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 74 0]
[ 1 1 54]]
FOLD 3 Number of neighbors 89
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 89
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.97 0.98 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 89
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 89
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.91 0.96 70
sport
0.97 1.00 0.99 75
technology
0.92 0.98 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[64 1 5]
[ 0 75 0]
[ 0 1 55]]
FOLD 7 Number of neighbors 89
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.96 0.99 0.97 75
technology
0.95 0.98 0.96 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 89 neighbours): 96.89% (+/- 1.13%)
FOLD 1 Number of neighbors 91
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
0.99 1.00 0.99 76
technology
0.93 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 91
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 74 0]
[ 1 1 54]]
FOLD 3 Number of neighbors 91
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 91
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 91
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 91
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.93 0.96 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[65 1 4]
[ 0 75 0]
[ 0 1 55]]
FOLD 7 Number of neighbors 91
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.96 0.99 0.97 75
technology
0.93 0.98 0.96 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[64 2 4]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 91 neighbours): 96.90% (+/- 1.13%)
FOLD 1 Number of neighbors 93
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 93
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.94 0.94 70
sport
0.96 0.96 0.96 75
technology
0.96 0.96 0.96 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 3 72 0]
[ 1 1 54]]
FOLD 3 Number of neighbors 93
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.99 1.00 0.99 75
technology
0.93 0.96 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 93
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 93
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 93
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 1 55]]
FOLD 7 Number of neighbors 93
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.96 0.99 0.97 75
technology
0.95 0.98 0.96 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 93 neighbours): 96.91% (+/- 1.13%)
FOLD 1 Number of neighbors 95
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
0.99 1.00 0.99 76
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 95
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 95
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 95
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 95
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 95
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 1 55]]
FOLD 7 Number of neighbors 95
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.96 0.99 0.97 75
technology
0.95 0.98 0.96 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 95 neighbours): 96.91% (+/- 1.12%)
FOLD 1 Number of neighbors 97
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
0.99 1.00 0.99 76
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 97
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.94 0.94 70
sport
0.95 0.96 0.95 75
technology
0.96 0.95 0.95 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 97
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.99 1.00 0.99 75
technology
0.93 0.96 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 97
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 97
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 97
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 1 55]]
FOLD 7 Number of neighbors 97
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.96 0.99 0.97 75
technology
0.93 0.98 0.96 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[64 2 4]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 97 neighbours): 96.92% (+/- 1.12%)
FOLD 1 Number of neighbors 99
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 99
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 99
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.99 1.00 0.99 75
technology
0.93 0.96 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 99
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.97 0.98 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 99
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 99
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 1 54]]
FOLD 7 Number of neighbors 99
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.98 0.96 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 99 neighbours): 96.92% (+/- 1.12%)
FOLD 1 Number of neighbors 101
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 101
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 101
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 101
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.97 0.98 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 101
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 101
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 1 55]]
FOLD 7 Number of neighbors 101
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.99 0.99 75
technology
0.95 0.98 0.96 55
avg / total 0.98 0.97 0.98 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 101 neighbours): 96.93% (+/- 1.11%)
FOLD 1 Number of neighbors 103
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 103
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 103
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 103
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.97 0.98 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 103
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 103
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 1 55]]
FOLD 7 Number of neighbors 103
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.99 0.99 75
technology
0.95 0.98 0.96 55
avg / total 0.98 0.97 0.98 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 103 neighbours): 96.94% (+/- 1.11%)
FOLD 1 Number of neighbors 105
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
0.99 1.00 0.99 76
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 105
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 105
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 105
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.97 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 105
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 105
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 1 55]]
FOLD 7 Number of neighbors 105
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.98 0.96 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 105 neighbours): 96.95% (+/- 1.10%)
FOLD 1 Number of neighbors 107
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 107
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 107
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 107
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.97 0.98 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 107
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 107
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.96 0.98 70
sport
0.99 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 0 1 55]]
FOLD 7 Number of neighbors 107
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.99 0.99 75
technology
0.95 0.98 0.96 55
avg / total 0.98 0.97 0.98 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 107 neighbours): 96.95% (+/- 1.10%)
FOLD 1 Number of neighbors 109
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 109
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.96 0.95 75
technology
0.95 0.95 0.95 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 72 1]
[ 1 2 53]]
FOLD 3 Number of neighbors 109
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 109
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 109
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 109
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 1 54]]
FOLD 7 Number of neighbors 109
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.98 0.96 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 109 neighbours): 96.96% (+/- 1.11%)
FOLD 1 Number of neighbors 111
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 111
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.94 0.94 70
sport
0.95 0.96 0.95 75
technology
0.96 0.95 0.95 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 111
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.90 0.94 70
sport
0.97 1.00 0.99 75
technology
0.92 0.98 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[63 2 5]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 111
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 111
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 111
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 1 55]]
FOLD 7 Number of neighbors 111
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.98 0.96 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 111 neighbours): 96.96% (+/- 1.11%)
FOLD 1 Number of neighbors 113
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 113
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.96 0.95 70
sport
0.96 0.95 0.95 75
technology
0.95 0.95 0.95 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 3 71 1]
[ 1 2 53]]
FOLD 3 Number of neighbors 113
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.97 1.00 0.99 75
technology
0.93 0.96 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 113
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 113
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 113
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 1 55]]
FOLD 7 Number of neighbors 113
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.96 0.95 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 113 neighbours): 96.97% (+/- 1.11%)
FOLD 1 Number of neighbors 115
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 115
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.95 0.97 0.96 75
technology
0.95 0.95 0.95 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 2 ):
[[65 2 3]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 115
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.99 1.00 0.99 75
technology
0.92 0.98 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[64 1 5]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 115
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.97 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 115
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 115
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 1 55]]
FOLD 7 Number of neighbors 115
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.99 0.99 75
technology
0.95 0.98 0.96 55
avg / total 0.98 0.97 0.98 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 115 neighbours): 96.97% (+/- 1.11%)
FOLD 1 Number of neighbors 117
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 117
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.96 0.95 70
sport
0.96 0.96 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 117
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 117
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 117
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 117
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 7 Number of neighbors 117
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.99 0.99 75
technology
0.95 0.98 0.96 55
avg / total 0.98 0.97 0.98 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 117 neighbours): 96.98% (+/- 1.11%)
FOLD 1 Number of neighbors 119
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 119
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.96 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 119
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.99 1.00 0.99 75
technology
0.92 0.96 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[64 1 5]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 119
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.99 0.97 0.98 75
technology
0.95 1.00 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[66 1 3]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 119
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 119
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.93 0.96 70
sport
0.99 1.00 0.99 75
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[65 1 4]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 119
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.98 0.96 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 119 neighbours): 96.98% (+/- 1.10%)
FOLD 1 Number of neighbors 121
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 121
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 121
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.99 1.00 0.99 75
technology
0.92 0.96 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[64 1 5]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 121
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 121
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 121
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 121
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 0.99 0.98 75
technology
0.95 0.96 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 0 2 53]]
Model accuracy (for 121 neighbours): 96.99% (+/- 1.10%)
FOLD 1 Number of neighbors 123
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 123
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.96 0.95 70
sport
0.96 0.96 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 123
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.97 1.00 0.99 75
technology
0.91 0.95 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[64 1 5]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 123
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.97 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 123
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 123
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 123
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 0.96 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 123 neighbours): 96.99% (+/- 1.10%)
FOLD 1 Number of neighbors 125
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 125
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 125
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.97 1.00 0.99 75
technology
0.91 0.95 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[64 1 5]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 125
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.97 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 125
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 125
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 125
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.98 0.96 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 125 neighbours): 96.99% (+/- 1.10%)
FOLD 1 Number of neighbors 127
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 127
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.96 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 127
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.96 1.00 0.98 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 127
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.97 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 127
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 127
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 127
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 127 neighbours): 97.00% (+/- 1.10%)
FOLD 1 Number of neighbors 129
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 129
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 129
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 129
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 129
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 129
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.96 0.98 70
sport
0.99 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 0 1 55]]
FOLD 7 Number of neighbors 129
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 129 neighbours): 97.00% (+/- 1.10%)
FOLD 1 Number of neighbors 131
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 131
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.96 0.96 0.96 75
technology
0.96 0.96 0.96 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 3 72 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 131
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 131
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.97 0.98 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 131
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 131
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.96 0.98 70
sport
0.99 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 0 1 55]]
FOLD 7 Number of neighbors 131
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 131 neighbours): 97.01% (+/- 1.10%)
FOLD 1 Number of neighbors 133
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 133
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.96 0.95 70
sport
0.96 0.96 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 133
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 133
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.97 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 133
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 133
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 1 55]]
FOLD 7 Number of neighbors 133
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 133 neighbours): 97.01% (+/- 1.10%)
FOLD 1 Number of neighbors 135
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 135
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.96 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 135
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 135
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.97 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 135
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 135
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 135
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 135 neighbours): 97.01% (+/- 1.10%)
FOLD 1 Number of neighbors 137
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 137
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 137
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 137
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 137
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 137
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 137
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 137 neighbours): 97.02% (+/- 1.10%)
FOLD 1 Number of neighbors 139
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 139
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.96 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 139
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 139
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.97 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 139
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 139
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 139
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 139 neighbours): 97.02% (+/- 1.10%)
FOLD 1 Number of neighbors 141
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.99 0.99 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 141
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.96 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 141
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 141
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.97 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 141
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 141
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 141
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 141 neighbours): 97.02% (+/- 1.09%)
FOLD 1 Number of neighbors 143
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 143
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 143
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 143
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 143
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 143
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.96 1.00 0.98 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 2 54]]
FOLD 7 Number of neighbors 143
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 0.99 0.98 75
technology
0.95 0.96 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 0 2 53]]
Model accuracy (for 143 neighbours): 97.03% (+/- 1.09%)
FOLD 1 Number of neighbors 145
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 145
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 145
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.97 1.00 0.99 75
technology
0.91 0.95 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[64 1 5]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 145
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 145
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 145
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 145
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 145 neighbours): 97.03% (+/- 1.09%)
FOLD 1 Number of neighbors 147
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.99 0.99 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 147
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.96 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 147
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.97 1.00 0.99 75
technology
0.91 0.95 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[64 1 5]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 147
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.97 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 147
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 147
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.96 1.00 0.98 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 2 54]]
FOLD 7 Number of neighbors 147
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 147 neighbours): 97.03% (+/- 1.09%)
FOLD 1 Number of neighbors 149
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 149
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 149
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.97 1.00 0.99 75
technology
0.91 0.95 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[64 1 5]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 149
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 149
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 149
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 1 55]]
FOLD 7 Number of neighbors 149
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.93 0.95 0.94 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 0 4]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 149 neighbours): 97.03% (+/- 1.09%)
FOLD 1 Number of neighbors 151
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.99 0.99 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 151
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.96 0.95 70
sport
0.96 0.96 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 151
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.97 1.00 0.99 75
technology
0.91 0.95 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[64 1 5]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 151
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.97 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 151
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 151
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 151
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 151 neighbours): 97.03% (+/- 1.09%)
FOLD 1 Number of neighbors 153
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 153
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.96 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 153
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 153
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 153
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 153
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 153
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.93 0.95 0.94 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 1 4]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 153 neighbours): 97.03% (+/- 1.10%)
FOLD 1 Number of neighbors 155
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 155
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.96 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 155
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 155
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.97 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 155
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 155
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 155
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 155 neighbours): 97.04% (+/- 1.09%)
FOLD 1 Number of neighbors 157
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 157
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 157
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.97 1.00 0.99 75
technology
0.91 0.95 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[64 1 5]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 157
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.97 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 157
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 157
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 7 Number of neighbors 157
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 157 neighbours): 97.04% (+/- 1.10%)
FOLD 1 Number of neighbors 159
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.99 0.97 0.98 76
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 159
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 159
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.90 0.93 70
sport
0.96 1.00 0.98 75
technology
0.91 0.95 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 3 ):
[[63 2 5]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 159
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.99 0.97 0.98 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 159
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 159
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.96 0.98 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 0 3 53]]
FOLD 7 Number of neighbors 159
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 159 neighbours): 97.03% (+/- 1.09%)
FOLD 1 Number of neighbors 161
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.97 1.00 0.99 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 161
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 161
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 1.00 0.98 75
technology
0.93 0.93 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 2 52]]
FOLD 4 Number of neighbors 161
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 161
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 161
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 1.00 1.00 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 7 Number of neighbors 161
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.93 0.95 0.94 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 1 4]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 161 neighbours): 97.03% (+/- 1.10%)
FOLD 1 Number of neighbors 163
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.97 1.00 0.99 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 163
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 163
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 163
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 163
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 163
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 7 Number of neighbors 163
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 163 neighbours): 97.03% (+/- 1.09%)
FOLD 1 Number of neighbors 165
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.99 0.99 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 165
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.96 0.95 70
sport
0.96 0.96 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 165
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 165
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
1.00 0.97 0.99 75
technology
0.98 1.00 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 165
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 165
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 3 52]]
FOLD 7 Number of neighbors 165
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.93 0.95 0.94 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 0 4]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 165 neighbours): 97.03% (+/- 1.09%)
FOLD 1 Number of neighbors 167
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 167
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 167
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 167
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.99 0.97 0.98 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 167
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 167
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 167
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 167 neighbours): 97.03% (+/- 1.09%)
FOLD 1 Number of neighbors 169
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.97 1.00 0.99 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 169
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.96 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 169
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 169
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.99 0.99 70
sport
1.00 0.99 0.99 75
technology
0.98 1.00 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 169
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 169
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 3 52]]
FOLD 7 Number of neighbors 169
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 0.96 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 169 neighbours): 97.04% (+/- 1.09%)
FOLD 1 Number of neighbors 171
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 171
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.94 0.94 70
sport
0.95 0.96 0.95 75
technology
0.96 0.95 0.95 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 171
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 171
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.99 0.99 70
sport
1.00 0.99 0.99 75
technology
0.98 1.00 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 171
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 171
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 171
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 171 neighbours): 97.04% (+/- 1.09%)
FOLD 1 Number of neighbors 173
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 173
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 173
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 173
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.99 0.97 0.98 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 173
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 173
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 173
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 173 neighbours): 97.03% (+/- 1.09%)
FOLD 1 Number of neighbors 175
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.99 0.99 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 175
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 175
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 175
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.97 0.98 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 175
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 175
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 7 Number of neighbors 175
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 175 neighbours): 97.04% (+/- 1.09%)
FOLD 1 Number of neighbors 177
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.99 0.99 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 177
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 177
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 177
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.97 0.98 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 177
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 177
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 177
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.93 0.95 0.94 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 0 4]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 177 neighbours): 97.03% (+/- 1.09%)
FOLD 1 Number of neighbors 179
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 179
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 179
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.97 1.00 0.99 75
technology
0.91 0.95 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[64 1 5]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 179
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.97 0.98 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 179
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 179
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 7 Number of neighbors 179
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 179 neighbours): 97.03% (+/- 1.09%)
FOLD 1 Number of neighbors 181
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.97 1.00 0.99 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 181
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.96 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 181
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 181
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.99 0.99 70
sport
0.99 0.99 0.99 75
technology
0.98 0.98 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 1 74 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 181
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 181
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 7 Number of neighbors 181
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 181 neighbours): 97.04% (+/- 1.08%)
FOLD 1 Number of neighbors 183
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.97 1.00 0.99 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 183
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 183
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 183
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.99 0.99 70
sport
0.99 0.99 0.99 75
technology
0.98 0.98 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 1 74 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 183
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 183
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 7 Number of neighbors 183
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.97 0.97 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 2 73 0]
[ 1 2 52]]
Model accuracy (for 183 neighbours): 97.04% (+/- 1.08%)
FOLD 1 Number of neighbors 185
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.97 0.99 0.98 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 185
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 185
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 185
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.97 0.98 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 185
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 185
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 185
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 185 neighbours): 97.04% (+/- 1.08%)
FOLD 1 Number of neighbors 187
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.97 1.00 0.99 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 187
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 187
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 187
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.99 0.99 70
sport
1.00 0.99 0.99 75
technology
0.98 1.00 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 187
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 187
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 187
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 187 neighbours): 97.04% (+/- 1.08%)
FOLD 1 Number of neighbors 189
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.99 0.99 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 189
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 189
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 189
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.97 0.98 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 189
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 189
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 7 Number of neighbors 189
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 189 neighbours): 97.04% (+/- 1.08%)
FOLD 1 Number of neighbors 191
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.97 0.99 0.98 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 191
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 191
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 191
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
1.00 0.97 0.99 75
technology
0.98 1.00 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 191
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 191
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 1.00 1.00 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 7 Number of neighbors 191
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 191 neighbours): 97.05% (+/- 1.08%)
FOLD 1 Number of neighbors 193
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 193
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 193
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 193
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.99 0.99 70
sport
1.00 0.99 0.99 75
technology
0.98 1.00 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 193
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 193
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 193
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 193 neighbours): 97.05% (+/- 1.08%)
FOLD 1 Number of neighbors 195
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 195
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.93 0.97 0.95 70
sport
0.97 0.95 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 4 71 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 195
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 195
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.97 0.98 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 195
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 195
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 195
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.93 0.95 0.94 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 0 4]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 195 neighbours): 97.05% (+/- 1.08%)
FOLD 1 Number of neighbors 197
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.97 1.00 0.99 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 197
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 197
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 197
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.97 0.98 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 197
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 197
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 7 Number of neighbors 197
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.93 0.95 0.94 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 0 4]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 197 neighbours): 97.05% (+/- 1.08%)
FOLD 1 Number of neighbors 199
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.97 0.99 0.98 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 199
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 199
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 199
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.97 0.98 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 199
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 199
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 199
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 199 neighbours): 97.05% (+/- 1.07%)
FOLD 1 Number of neighbors 201
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 201
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 201
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 201
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.97 0.98 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 201
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 201
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 7 Number of neighbors 201
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 201 neighbours): 97.05% (+/- 1.07%)
FOLD 1 Number of neighbors 203
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.97 0.99 0.98 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 203
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 203
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 203
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.99 0.99 70
sport
0.99 0.99 0.99 75
technology
0.98 0.98 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 1 74 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 203
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 203
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 7 Number of neighbors 203
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 203 neighbours): 97.05% (+/- 1.07%)
FOLD 1 Number of neighbors 205
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.97 0.99 0.98 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 205
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 205
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 205
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
1.00 0.97 0.99 75
technology
0.98 1.00 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 205
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 205
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 205
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 205 neighbours): 97.06% (+/- 1.07%)
FOLD 1 Number of neighbors 207
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.97 0.99 0.98 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 207
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 207
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.97 1.00 0.99 75
technology
0.91 0.95 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[64 1 5]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 207
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.97 0.98 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 207
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 207
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 207
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 207 neighbours): 97.06% (+/- 1.07%)
FOLD 1 Number of neighbors 209
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.97 0.97 0.97 76
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 209
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 209
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 209
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.97 0.98 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 209
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 209
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 209
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 209 neighbours): 97.06% (+/- 1.07%)
FOLD 1 Number of neighbors 211
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.97 0.99 0.98 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 211
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.93 0.97 0.95 70
sport
0.97 0.95 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 4 71 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 211
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 211
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.99 0.99 70
sport
1.00 0.99 0.99 75
technology
0.98 1.00 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 211
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 211
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 211
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 211 neighbours): 97.06% (+/- 1.07%)
FOLD 1 Number of neighbors 213
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.96 0.97 0.97 76
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 213
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 213
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 213
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
1.00 0.97 0.99 75
technology
0.98 1.00 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 213
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 213
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 213
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 213 neighbours): 97.06% (+/- 1.07%)
FOLD 1 Number of neighbors 215
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.97 1.00 0.99 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 215
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.93 0.97 0.95 70
sport
0.97 0.95 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 4 71 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 215
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 215
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.99 0.99 70
sport
1.00 0.99 0.99 75
technology
0.98 1.00 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 215
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 215
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 215
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.97 0.97 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 2 73 0]
[ 1 2 52]]
Model accuracy (for 215 neighbours): 97.06% (+/- 1.07%)
FOLD 1 Number of neighbors 217
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.97 0.97 0.97 76
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 217
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 217
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 217
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.99 0.97 0.98 75
technology
1.00 0.98 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 217
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 217
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 217
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 217 neighbours): 97.06% (+/- 1.07%)
FOLD 1 Number of neighbors 219
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.97 0.97 0.97 76
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 219
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 219
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 2 52]]
FOLD 4 Number of neighbors 219
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.97 0.98 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 219
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 219
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 219
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.93 0.95 0.94 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 0 4]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 219 neighbours): 97.05% (+/- 1.07%)
FOLD 1 Number of neighbors 221
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.96 0.97 0.97 76
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 221
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 221
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 221
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.99 0.97 0.98 75
technology
1.00 0.98 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 221
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 221
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 221
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.93 0.95 0.94 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 0 4]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 221 neighbours): 97.05% (+/- 1.07%)
FOLD 1 Number of neighbors 223
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.96 0.99 0.97 76
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 223
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 223
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 223
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
1.00 0.97 0.99 75
technology
1.00 1.00 1.00 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 223
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 223
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 223
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.93 0.95 0.94 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 0 4]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 223 neighbours): 97.05% (+/- 1.07%)
FOLD 1 Number of neighbors 225
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.95 1.00 0.97 76
technology
0.96 0.93 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 225
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 225
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 225
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.99 0.99 70
sport
0.99 0.99 0.99 75
technology
0.98 0.98 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 1 74 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 225
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 225
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 225
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.93 0.95 0.94 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 0 4]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 225 neighbours): 97.05% (+/- 1.07%)
FOLD 1 Number of neighbors 227
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.95 0.99 0.97 76
technology
0.96 0.93 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 227
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 227
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 227
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.97 0.98 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 227
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 227
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 227
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.96 0.99 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 3 51]]
Model accuracy (for 227 neighbours): 97.05% (+/- 1.07%)
FOLD 1 Number of neighbors 229
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.97 1.00 0.99 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 229
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.93 0.97 0.95 70
sport
0.97 0.95 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 4 71 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 229
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 229
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.97 0.98 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 229
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 229
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 229
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 229 neighbours): 97.05% (+/- 1.07%)
FOLD 1 Number of neighbors 231
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.95 0.97 0.96 76
technology
0.96 0.93 0.95 56
avg / total 0.96 0.96 0.96 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 231
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 231
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 1 74 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 231
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.99 0.97 0.98 75
technology
1.00 0.98 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 231
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 231
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 231
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 231 neighbours): 97.05% (+/- 1.07%)
FOLD 1 Number of neighbors 233
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.95 0.97 0.96 76
technology
0.96 0.93 0.95 56
avg / total 0.96 0.96 0.96 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 233
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 233
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 1.00 0.98 75
technology
0.93 0.93 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 2 52]]
FOLD 4 Number of neighbors 233
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.97 0.98 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 233
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 233
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 233
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 233 neighbours): 97.05% (+/- 1.07%)
FOLD 1 Number of neighbors 235
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.97 0.99 0.98 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 235
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 235
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 235
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 1.00 0.99 70
sport
0.99 0.99 0.99 75
technology
1.00 0.98 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 235
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 235
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 235
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.96 0.99 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 3 51]]
Model accuracy (for 235 neighbours): 97.05% (+/- 1.07%)
FOLD 1 Number of neighbors 237
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.96 0.99 0.97 76
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 237
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 237
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 237
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.99 0.97 0.98 75
technology
1.00 0.98 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 237
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 237
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 237
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 237 neighbours): 97.05% (+/- 1.07%)
FOLD 1 Number of neighbors 239
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.97 0.97 0.97 76
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 239
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 239
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 239
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
1.00 0.97 0.99 75
technology
0.98 1.00 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 239
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 239
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 239
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 239 neighbours): 97.05% (+/- 1.06%)
FOLD 1 Number of neighbors 241
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.96 0.99 0.97 76
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 241
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.93 0.97 0.95 70
sport
0.97 0.95 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 4 71 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 241
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 241
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.99 0.97 0.98 75
technology
1.00 0.98 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 241
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 241
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 241
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.93 0.95 0.94 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 0 4]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 241 neighbours): 97.05% (+/- 1.06%)
FOLD 1 Number of neighbors 243
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.96 0.99 0.97 76
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 243
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 243
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 243
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.97 0.98 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 243
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 243
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 243
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 243 neighbours): 97.05% (+/- 1.06%)
FOLD 1 Number of neighbors 245
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.96 0.97 0.97 76
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 245
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 245
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 245
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.99 0.97 0.98 75
technology
1.00 0.98 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 245
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 245
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 245
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 245 neighbours): 97.05% (+/- 1.06%)
FOLD 1 Number of neighbors 247
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.96 0.99 0.97 76
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 247
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 247
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 2 52]]
FOLD 4 Number of neighbors 247
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.99 0.97 0.98 75
technology
1.00 0.98 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 247
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 247
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 247
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.93 0.95 0.94 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 0 4]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 247 neighbours): 97.05% (+/- 1.06%)
FOLD 1 Number of neighbors 249
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.96 0.97 0.97 76
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 249
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 249
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 249
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.99 0.99 70
sport
1.00 0.99 0.99 75
technology
0.98 1.00 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 249
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 249
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 249
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.96 0.99 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 3 51]]
Model accuracy (for 249 neighbours): 97.05% (+/- 1.06%)
FOLD 1 Number of neighbors 251
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.96 1.00 0.98 76
technology
0.96 0.95 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 251
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 251
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 251
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.99 0.97 0.98 75
technology
1.00 0.98 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 251
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 251
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 251
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 251 neighbours): 97.05% (+/- 1.06%)
FOLD 1 Number of neighbors 253
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.97 1.00 0.99 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 253
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 253
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 4 Number of neighbors 253
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 1.00 0.99 70
sport
0.99 0.99 0.99 75
technology
1.00 0.98 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 253
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 253
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.96 0.98 70
sport
0.97 1.00 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 0 2 54]]
FOLD 7 Number of neighbors 253
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.93 0.95 0.94 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 0 4]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 253 neighbours): 97.05% (+/- 1.06%)
FOLD 1 Number of neighbors 255
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.96 0.99 0.97 76
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 255
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.93 0.97 0.95 70
sport
0.97 0.95 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 4 71 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 255
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 2 52]]
FOLD 4 Number of neighbors 255
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.97 0.98 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 255
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 255
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 255
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 255 neighbours): 97.05% (+/- 1.06%)
FOLD 1 Number of neighbors 257
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.97 1.00 0.99 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 257
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 257
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 257
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 1.00 0.99 70
sport
0.99 0.99 0.99 75
technology
1.00 0.98 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 257
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 257
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 257
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 257 neighbours): 97.05% (+/- 1.06%)
FOLD 1 Number of neighbors 259
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.96 0.97 0.97 76
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 259
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 259
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 259
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 1.00 0.99 70
sport
0.99 0.99 0.99 75
technology
1.00 0.98 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 259
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 259
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 259
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 259 neighbours): 97.05% (+/- 1.06%)
FOLD 1 Number of neighbors 261
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.95 1.00 0.97 76
technology
0.96 0.93 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 261
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.93 0.97 0.95 70
sport
0.97 0.95 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 4 71 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 261
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 261
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.97 0.98 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 261
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 261
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 261
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 261 neighbours): 97.05% (+/- 1.06%)
FOLD 1 Number of neighbors 263
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.96 1.00 0.98 76
technology
0.96 0.95 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 263
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 263
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 263
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 1.00 0.99 70
sport
0.99 0.99 0.99 75
technology
1.00 0.98 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 263
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 263
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.96 0.98 70
sport
0.97 1.00 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 0 2 54]]
FOLD 7 Number of neighbors 263
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 0.99 0.98 75
technology
0.91 0.95 0.93 55
avg / total 0.96 0.95 0.96 200
Confusion Matrix (Fold 7 ):
[[65 0 5]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 263 neighbours): 97.05% (+/- 1.06%)
FOLD 1 Number of neighbors 265
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.96 0.99 0.97 76
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 265
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.93 0.97 0.95 70
sport
0.97 0.95 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 4 71 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 265
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 4 Number of neighbors 265
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.99 0.97 0.98 75
technology
1.00 0.98 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 265
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 265
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 265
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.93 0.95 0.94 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 0 4]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 265 neighbours): 97.05% (+/- 1.06%)
FOLD 1 Number of neighbors 267
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.96 0.97 0.97 76
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 267
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.93 0.97 0.95 70
sport
0.97 0.95 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 4 71 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 267
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 2 52]]
FOLD 4 Number of neighbors 267
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.99 0.97 0.98 75
technology
1.00 0.98 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 267
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 267
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 267
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.93 0.95 0.94 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 0 4]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 267 neighbours): 97.05% (+/- 1.06%)
FOLD 1 Number of neighbors 269
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.96 0.99 0.97 76
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 269
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 269
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 2 52]]
FOLD 4 Number of neighbors 269
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 1.00 0.99 70
sport
0.99 0.99 0.99 75
technology
1.00 0.98 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 269
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 269
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 269
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 269 neighbours): 97.05% (+/- 1.06%)
FOLD 1 Number of neighbors 271
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.96 0.97 0.97 76
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 271
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 271
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 1 74 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 271
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.99 0.97 0.98 75
technology
1.00 0.98 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 271
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 271
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 271
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 271 neighbours): 97.05% (+/- 1.06%)
FOLD 1 Number of neighbors 273
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.96 0.99 0.97 76
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 273
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.96 0.95 70
sport
0.97 0.96 0.97 75
technology
0.95 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[67 0 3]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 273
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 273
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.99 0.97 0.98 75
technology
1.00 0.98 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 273
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 273
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.93 0.94 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 2 2 52]]
FOLD 7 Number of neighbors 273
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 273 neighbours): 97.05% (+/- 1.06%)
FOLD 1 Number of neighbors 275
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.97 1.00 0.99 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 275
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 275
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 275
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 1.00 0.99 70
sport
0.99 0.99 0.99 75
technology
1.00 0.98 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 275
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 275
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 275
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 275 neighbours): 97.05% (+/- 1.06%)
FOLD 1 Number of neighbors 277
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.95 0.99 0.97 76
technology
0.96 0.93 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 277
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.93 0.97 0.95 70
sport
0.97 0.95 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 4 71 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 277
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.96 0.99 0.97 75
technology
0.93 0.93 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 1 74 0]
[ 2 2 52]]
FOLD 4 Number of neighbors 277
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 1.00 0.99 70
sport
0.99 0.99 0.99 75
technology
1.00 0.98 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 277
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 277
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 277
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 0.99 0.98 75
technology
0.91 0.95 0.93 55
avg / total 0.96 0.95 0.96 200
Confusion Matrix (Fold 7 ):
[[65 0 5]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 277 neighbours): 97.05% (+/- 1.07%)
FOLD 1 Number of neighbors 279
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.97 0.99 0.98 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 279
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 279
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 279
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 1.00 0.99 70
sport
0.99 0.99 0.99 75
technology
1.00 0.98 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 279
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 279
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 279
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.97 0.97 0.97 75
technology
0.93 0.95 0.94 55
avg / total 0.96 0.95 0.96 200
Confusion Matrix (Fold 7 ):
[[66 0 4]
[ 2 73 0]
[ 1 2 52]]
Model accuracy (for 279 neighbours): 97.05% (+/- 1.07%)
FOLD 1 Number of neighbors 281
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.96 0.99 0.97 76
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 281
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 281
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 281
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.99 0.97 0.98 75
technology
1.00 0.98 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 281
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 281
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 281
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 281 neighbours): 97.05% (+/- 1.07%)
FOLD 1 Number of neighbors 283
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.97 0.99 0.98 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 283
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.93 0.97 0.95 70
sport
0.97 0.95 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 4 71 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 283
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 2 52]]
FOLD 4 Number of neighbors 283
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 1.00 0.99 70
sport
0.99 0.99 0.99 75
technology
1.00 0.98 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 283
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 283
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 283
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 0.99 0.98 75
technology
0.91 0.95 0.93 55
avg / total 0.96 0.95 0.96 200
Confusion Matrix (Fold 7 ):
[[65 0 5]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 283 neighbours): 97.05% (+/- 1.07%)
FOLD 1 Number of neighbors 285
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.97 0.97 0.97 76
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 285
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.93 0.97 0.95 70
sport
0.97 0.95 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 4 71 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 285
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 2 52]]
FOLD 4 Number of neighbors 285
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 1.00 0.99 70
sport
0.99 0.99 0.99 75
technology
1.00 0.98 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 285
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 285
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.96 1.00 0.98 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 7 Number of neighbors 285
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.93 0.95 0.94 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 0 4]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 285 neighbours): 97.05% (+/- 1.07%)
FOLD 1 Number of neighbors 287
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.96 0.99 0.97 76
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 287
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 287
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 2 52]]
FOLD 4 Number of neighbors 287
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 1.00 0.99 70
sport
1.00 0.99 0.99 75
technology
1.00 1.00 1.00 56
avg / total 1.00 1.00 1.00 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 287
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 287
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 287
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.97 0.97 0.97 75
technology
0.91 0.95 0.93 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 0 5]
[ 2 73 0]
[ 1 2 52]]
Model accuracy (for 287 neighbours): 97.05% (+/- 1.07%)
FOLD 1 Number of neighbors 289
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.97 0.99 0.98 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 289
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 289
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 4 Number of neighbors 289
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 1.00 0.99 70
sport
0.99 0.99 0.99 75
technology
1.00 0.98 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 289
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 289
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 289
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 289 neighbours): 97.05% (+/- 1.07%)
FOLD 1 Number of neighbors 291
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.97 0.97 0.97 76
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 291
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.93 0.97 0.95 70
sport
0.97 0.95 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 4 71 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 291
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.95 1.00 0.97 75
technology
0.93 0.91 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 3 51]]
FOLD 4 Number of neighbors 291
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.99 0.97 0.98 75
technology
1.00 0.98 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 291
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 291
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.96 1.00 0.98 75
technology
0.95 0.93 0.94 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 1 3 52]]
FOLD 7 Number of neighbors 291
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.97 0.97 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 2 73 0]
[ 1 2 52]]
Model accuracy (for 291 neighbours): 97.04% (+/- 1.08%)
FOLD 1 Number of neighbors 293
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.96 0.97 0.97 76
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 293
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.93 0.97 0.95 70
sport
0.97 0.95 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 4 71 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 293
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 2 52]]
FOLD 4 Number of neighbors 293
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.99 0.97 0.98 75
technology
1.00 0.98 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 293
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 293
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.95 1.00 0.97 75
technology
0.93 0.93 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[65 1 4]
[ 0 75 0]
[ 1 3 52]]
FOLD 7 Number of neighbors 293
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.93 0.95 0.94 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 0 4]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 293 neighbours): 97.04% (+/- 1.08%)
FOLD 1 Number of neighbors 295
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.97 0.97 0.97 76
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 74 1]
[ 0 2 54]]
FOLD 2 Number of neighbors 295
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.93 0.97 0.95 70
sport
0.97 0.95 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 4 71 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 295
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 295
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.99 0.97 0.98 75
technology
1.00 0.98 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 295
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 295
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 295
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.97 0.97 0.97 75
technology
0.91 0.95 0.93 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 0 5]
[ 2 73 0]
[ 1 2 52]]
Model accuracy (for 295 neighbours): 97.04% (+/- 1.08%)
FOLD 1 Number of neighbors 297
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.96 0.97 0.97 76
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 297
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.93 0.97 0.95 70
sport
0.97 0.95 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 4 71 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 297
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 297
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.99 0.97 0.98 75
technology
1.00 0.98 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 297
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 297
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.96 1.00 0.98 75
technology
0.95 0.93 0.94 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 1 3 52]]
FOLD 7 Number of neighbors 297
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.97 0.97 0.97 75
technology
0.93 0.95 0.94 55
avg / total 0.96 0.95 0.96 200
Confusion Matrix (Fold 7 ):
[[66 0 4]
[ 2 73 0]
[ 1 2 52]]
Model accuracy (for 297 neighbours): 97.04% (+/- 1.08%)
FOLD 1 Number of neighbors 299
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.97 0.99 0.98 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 299
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 299
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.96 1.00 0.98 75
technology
0.96 0.93 0.95 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 2 2 52]]
FOLD 4 Number of neighbors 299
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.99 0.97 0.98 75
technology
1.00 0.98 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 299
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 299
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 3 52]]
FOLD 7 Number of neighbors 299
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.97 0.97 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 2 73 0]
[ 1 2 52]]
Model accuracy (for 299 neighbours): 97.04% (+/- 1.08%)
In [380]:
# changing to misclassification error
MSE = [1-x/100 for x in k_model_accuracy]
index=MSE.index(min(MSE))
optimal_k = neighbors[index]
print ("The highest model accuracy",k_model_accuracy[index],"is achieved by using optimal number of neighbors %d" % optimal_k)
# plot misclassification error vs k
plt.plot(neighbors, MSE)
plt.xlabel('Number of Neighbors K')
plt.ylabel('Misclassification Error')
plt.show()
The highest model accuracy 97.0565916786394 is achieved by using optimal number of neighbors 205
In [381]:
cvscores_ngram = []
k_model_accuracy_ngram=[]
kfold = StratifiedKFold(n_splits=7, shuffle=True, random_state=seed)
for k in neighbors:
fold=0
model = KNeighborsClassifier(n_neighbors=k, metric='cosine')
for train, test in kfold.split(X_term_weighting_ngram, class_labels):
fold+=1
print('FOLD',fold, 'Number of neighbors', k)
labels_train=[]
for i in range(len(train)):
labels_train.append(class_labels[train[i]])
labels_test=[]
for i in range(len(test)):
labels_test.append(class_labels[test[i]])
# Plot a bar plot of the labels: class distribution is adjusted
#seaborn.countplot - Show value counts for a single categorical variable:
print('Class Distribution --> Train data (Fold',fold,'):')
ax = sns.countplot(labels_train)
ax.set_title("Distribution of the Labels (without N/A)")
plt.show()
# Apply the random under-sampling
#Oversampling and undersampling in data analysis are techniques used to adjust the class distribution of a data set
#(i.e. the ratio between the different classes/categories represented).
rus = RandomUnderSampler(return_indices=True)
train_rus, train_labels_rus, idx_resampled = rus.fit_sample(X_term_weighting_ngram[train], labels_train)
train_rus, train_labels_rus = shuffle(train_rus, train_labels_rus)
# Plot a bar plot of the labels
#seaborn.countplot - Show value counts for a single categorical variable:
print('Class Distribution after performing under-sampling --> Train data (Fold',fold,'):')
ax = sns.countplot(train_labels_rus)
sns.countplot(train_labels_rus) #--> class distribution is adjusted
plt.show()
# Fit/Train the model
model.fit(train_rus, train_labels_rus)
#Evaluate the Model; Use the test dataset to evaluate the model
print('\n\n ****** Test Data ******** (Fold',fold,'):')
predicted = model.predict(X_term_weighting_ngram[test])
# Print performance details
print(metrics.classification_report(labels_test, predicted))
# Print confusion matrix
print('Confusion Matrix (Fold',fold,'):')
print(metrics.confusion_matrix(labels_test, predicted))
cvscores_ngram.append(accuracy_score(labels_test, predicted) * 100)
print("\n\n Model accuracy (for",k," neighbours): %.2f%% (+/- %.2f%%)" % (numpy.mean(cvscores_ngram), numpy.std(cvscores_ngram)))
k_model_accuracy_ngram.append(numpy.mean(cvscores_ngram))
FOLD 1 Number of neighbors 1 Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.96 0.92 0.94 71
sport
1.00 0.96 0.98 76
technology
0.89 0.98 0.93 56
avg / total 0.95 0.95 0.95 203
Confusion Matrix (Fold 1 ):
[[65 0 6]
[ 2 73 1]
[ 1 0 55]]
FOLD 2 Number of neighbors 1
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.98 0.87 0.92 70
sport
0.96 0.97 0.97 75
technology
0.87 0.98 0.92 56
avg / total 0.94 0.94 0.94 201
Confusion Matrix (Fold 2 ):
[[61 2 7]
[ 1 73 1]
[ 0 1 55]]
FOLD 3 Number of neighbors 1
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.94 0.94 0.94 70
sport
0.99 0.95 0.97 75
technology
0.93 0.98 0.96 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 3 71 1]
[ 1 0 55]]
FOLD 4 Number of neighbors 1
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.93 0.97 0.95 70
sport
0.99 0.92 0.95 75
technology
0.93 0.96 0.95 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 4 ):
[[68 1 1]
[ 3 69 3]
[ 2 0 54]]
FOLD 5 Number of neighbors 1
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.95 0.90 0.93 70
sport
0.97 0.95 0.96 75
technology
0.87 0.96 0.92 56
avg / total 0.94 0.94 0.94 201
Confusion Matrix (Fold 5 ):
[[63 1 6]
[ 2 71 2]
[ 1 1 54]]
FOLD 6 Number of neighbors 1
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.93 0.91 0.92 70
sport
0.95 0.97 0.96 75
technology
0.93 0.91 0.92 56
avg / total 0.94 0.94 0.94 201
Confusion Matrix (Fold 6 ):
[[64 3 3]
[ 1 73 1]
[ 4 1 51]]
FOLD 7 Number of neighbors 1
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.90 0.91 0.91 70
sport
0.95 0.96 0.95 75
technology
0.91 0.87 0.89 55
avg / total 0.92 0.92 0.92 200
Confusion Matrix (Fold 7 ):
[[64 2 4]
[ 2 72 1]
[ 5 2 48]]
Model accuracy (for 1 neighbours): 94.10% (+/- 1.13%)
FOLD 1 Number of neighbors 3
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.96 0.96 71
sport
0.99 0.97 0.98 76
technology
0.93 0.96 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 1 74 1]
[ 1 1 54]]
FOLD 2 Number of neighbors 3
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.89 0.93 70
sport
0.97 0.96 0.97 75
technology
0.87 0.98 0.92 56
avg / total 0.94 0.94 0.94 201
Confusion Matrix (Fold 2 ):
[[62 1 7]
[ 2 72 1]
[ 0 1 55]]
FOLD 3 Number of neighbors 3
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.99 0.97 0.98 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 2 73 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 3
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
1.00 0.95 0.97 75
technology
0.96 0.98 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 3 71 1]
[ 1 0 55]]
FOLD 5 Number of neighbors 3
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
1.00 0.99 0.99 75
technology
0.90 1.00 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[64 0 6]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 3
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.97 0.97 0.97 75
technology
0.90 0.93 0.91 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[65 0 5]
[ 1 73 1]
[ 2 2 52]]
FOLD 7 Number of neighbors 3
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.94 0.91 0.93 70
sport
0.95 0.97 0.96 75
technology
0.93 0.93 0.93 55
avg / total 0.94 0.94 0.94 200
Confusion Matrix (Fold 7 ):
[[64 2 4]
[ 2 73 0]
[ 2 2 51]]
Model accuracy (for 3 neighbours): 94.85% (+/- 1.40%)
FOLD 1 Number of neighbors 5
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.96 0.93 0.94 71
sport
0.99 0.96 0.97 76
technology
0.90 0.96 0.93 56
avg / total 0.95 0.95 0.95 203
Confusion Matrix (Fold 1 ):
[[66 0 5]
[ 2 73 1]
[ 1 1 54]]
FOLD 2 Number of neighbors 5
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.97 0.98 75
technology
0.92 0.98 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 0 4]
[ 1 73 1]
[ 0 1 55]]
FOLD 3 Number of neighbors 5
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.99 0.97 0.98 75
technology
0.95 1.00 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 2 73 0]
[ 0 0 56]]
FOLD 4 Number of neighbors 5
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
1.00 0.95 0.97 75
technology
0.96 0.98 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 3 71 1]
[ 1 0 55]]
FOLD 5 Number of neighbors 5
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.99 0.99 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[65 1 4]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 5
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.94 0.91 0.93 70
sport
0.97 0.99 0.98 75
technology
0.89 0.91 0.90 56
avg / total 0.94 0.94 0.94 201
Confusion Matrix (Fold 6 ):
[[64 0 6]
[ 1 74 0]
[ 3 2 51]]
FOLD 7 Number of neighbors 5
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.99 0.97 0.98 75
technology
0.95 0.96 0.95 55
avg / total 0.97 0.96 0.97 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 2 73 0]
[ 1 1 53]]
Model accuracy (for 5 neighbours): 95.26% (+/- 1.42%)
FOLD 1 Number of neighbors 7
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.93 0.96 71
sport
1.00 1.00 1.00 76
technology
0.92 0.98 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[66 0 5]
[ 0 76 0]
[ 1 0 55]]
FOLD 2 Number of neighbors 7
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.97 0.97 0.97 75
technology
0.92 0.98 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[65 1 4]
[ 1 73 1]
[ 0 1 55]]
FOLD 3 Number of neighbors 7
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 1.00 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 4 Number of neighbors 7
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
1.00 0.95 0.97 75
technology
0.96 0.98 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 3 71 1]
[ 1 0 55]]
FOLD 5 Number of neighbors 7
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 7
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.94 0.91 0.93 70
sport
0.97 0.99 0.98 75
technology
0.89 0.91 0.90 56
avg / total 0.94 0.94 0.94 201
Confusion Matrix (Fold 6 ):
[[64 0 6]
[ 1 74 0]
[ 3 2 51]]
FOLD 7 Number of neighbors 7
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 0.97 0.97 75
technology
0.93 0.98 0.96 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 1 4]
[ 2 73 0]
[ 0 1 54]]
Model accuracy (for 7 neighbours): 95.54% (+/- 1.43%)
FOLD 1 Number of neighbors 9
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.94 0.96 71
sport
1.00 1.00 1.00 76
technology
0.93 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 1 0 55]]
FOLD 2 Number of neighbors 9
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
1.00 0.97 0.99 75
technology
0.90 1.00 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[65 0 5]
[ 1 73 1]
[ 0 0 56]]
FOLD 3 Number of neighbors 9
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 0.99 0.99 75
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 74 1]
[ 0 0 56]]
FOLD 4 Number of neighbors 9
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.99 0.96 0.97 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 2 72 1]
[ 1 0 55]]
FOLD 5 Number of neighbors 9
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 9
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.96 0.91 0.93 70
sport
0.96 0.99 0.97 75
technology
0.91 0.93 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[64 1 5]
[ 1 74 0]
[ 2 2 52]]
FOLD 7 Number of neighbors 9
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.93 0.96 0.95 55
avg / total 0.97 0.96 0.97 200
Confusion Matrix (Fold 7 ):
[[66 0 4]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 9 neighbours): 95.77% (+/- 1.42%)
FOLD 1 Number of neighbors 11
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.93 0.96 71
sport
1.00 1.00 1.00 76
technology
0.92 0.98 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[66 0 5]
[ 0 76 0]
[ 1 0 55]]
FOLD 2 Number of neighbors 11
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 0.97 0.98 75
technology
0.92 1.00 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[65 1 4]
[ 1 73 1]
[ 0 0 56]]
FOLD 3 Number of neighbors 11
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 11
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
1.00 0.97 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 2 73 0]
[ 2 0 54]]
FOLD 5 Number of neighbors 11
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
1.00 0.93 0.96 70
sport
0.99 1.00 0.99 75
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[65 1 4]
[ 0 75 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 11
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.94 0.91 0.93 70
sport
0.96 0.99 0.97 75
technology
0.91 0.91 0.91 56
avg / total 0.94 0.94 0.94 201
Confusion Matrix (Fold 6 ):
[[64 1 5]
[ 1 74 0]
[ 3 2 51]]
FOLD 7 Number of neighbors 11
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.93 0.98 0.96 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[66 0 4]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 11 neighbours): 95.92% (+/- 1.42%)
FOLD 1 Number of neighbors 13
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.93 0.96 71
sport
1.00 1.00 1.00 76
technology
0.92 1.00 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[66 0 5]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 13
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
1.00 0.97 0.99 75
technology
0.92 1.00 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 0 4]
[ 1 73 1]
[ 0 0 56]]
FOLD 3 Number of neighbors 13
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 13
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
1.00 0.96 0.98 75
technology
0.98 1.00 0.99 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 3 72 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 13
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 0.99 0.99 75
technology
0.93 1.00 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[65 1 4]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 13
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.94 0.91 0.93 70
sport
0.96 0.99 0.97 75
technology
0.91 0.91 0.91 56
avg / total 0.94 0.94 0.94 201
Confusion Matrix (Fold 6 ):
[[64 1 5]
[ 1 74 0]
[ 3 2 51]]
FOLD 7 Number of neighbors 13
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 0.99 0.98 75
technology
0.93 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 1 4]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 13 neighbours): 96.02% (+/- 1.41%)
FOLD 1 Number of neighbors 15
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
0.99 0.99 0.99 76
technology
0.93 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 75 1]
[ 0 1 55]]
FOLD 2 Number of neighbors 15
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.98 0.90 0.94 70
sport
0.96 0.97 0.97 75
technology
0.90 0.98 0.94 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 2 ):
[[63 2 5]
[ 1 73 1]
[ 0 1 55]]
FOLD 3 Number of neighbors 15
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.99 0.99 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 1 74 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 15
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
1.00 0.96 0.98 75
technology
0.98 1.00 0.99 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 3 72 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 15
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 0.99 0.98 75
technology
0.92 0.98 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 5 ):
[[64 1 5]
[ 1 74 0]
[ 0 1 55]]
FOLD 6 Number of neighbors 15
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.96 1.00 0.98 75
technology
0.91 0.93 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[64 1 5]
[ 0 75 0]
[ 2 2 52]]
FOLD 7 Number of neighbors 15
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.93 0.96 0.95 55
avg / total 0.97 0.96 0.97 200
Confusion Matrix (Fold 7 ):
[[66 0 4]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 15 neighbours): 96.07% (+/- 1.38%)
FOLD 1 Number of neighbors 17
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.93 0.96 71
sport
1.00 1.00 1.00 76
technology
0.92 1.00 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[66 0 5]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 17
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 0.97 0.97 75
technology
0.90 0.98 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[64 1 5]
[ 1 73 1]
[ 0 1 55]]
FOLD 3 Number of neighbors 17
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 0.99 0.98 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 17
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.93 0.97 0.95 70
sport
1.00 0.96 0.98 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 3 72 0]
[ 2 0 54]]
FOLD 5 Number of neighbors 17
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.99 0.99 0.99 75
technology
0.92 1.00 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[64 1 5]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 17
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.89 0.93 70
sport
0.96 1.00 0.98 75
technology
0.90 0.95 0.92 56
avg / total 0.95 0.95 0.94 201
Confusion Matrix (Fold 6 ):
[[62 2 6]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 17
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.93 0.98 0.96 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[66 0 4]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 17 neighbours): 96.09% (+/- 1.34%)
FOLD 1 Number of neighbors 19
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.93 0.96 71
sport
1.00 1.00 1.00 76
technology
0.92 1.00 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[66 0 5]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 19
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 0.97 0.98 75
technology
0.92 1.00 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[65 1 4]
[ 1 73 1]
[ 0 0 56]]
FOLD 3 Number of neighbors 19
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 19
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.97 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 19
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.99 0.99 0.99 75
technology
0.92 0.98 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 5 ):
[[64 1 5]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 19
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.96 1.00 0.98 75
technology
0.91 0.93 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[64 1 5]
[ 0 75 0]
[ 2 2 52]]
FOLD 7 Number of neighbors 19
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.97 0.99 0.98 75
technology
0.93 0.98 0.96 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 1 4]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 19 neighbours): 96.15% (+/- 1.31%)
FOLD 1 Number of neighbors 21
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
1.00 1.00 1.00 76
technology
0.95 1.00 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 21
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 0.97 0.98 75
technology
0.92 1.00 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[65 1 4]
[ 1 73 1]
[ 0 0 56]]
FOLD 3 Number of neighbors 21
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 1.00 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 21
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.97 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 2 73 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 21
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 0.99 0.99 75
technology
0.93 1.00 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[65 1 4]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 21
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.96 1.00 0.98 75
technology
0.91 0.93 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[64 1 5]
[ 0 75 0]
[ 2 2 52]]
FOLD 7 Number of neighbors 21
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.96 0.99 0.97 75
technology
0.93 0.96 0.95 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[64 2 4]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 21 neighbours): 96.21% (+/- 1.32%)
FOLD 1 Number of neighbors 23
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.93 0.96 71
sport
1.00 0.99 0.99 76
technology
0.90 1.00 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[66 0 5]
[ 0 75 1]
[ 0 0 56]]
FOLD 2 Number of neighbors 23
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 0.97 0.98 75
technology
0.92 1.00 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[65 1 4]
[ 1 73 1]
[ 0 0 56]]
FOLD 3 Number of neighbors 23
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 23
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.97 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 2 73 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 23
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.94 0.93 0.94 70
sport
0.99 0.97 0.98 75
technology
0.93 0.96 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 5 ):
[[65 1 4]
[ 2 73 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 23
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.96 0.91 0.93 70
sport
0.97 1.00 0.99 75
technology
0.91 0.93 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[64 1 5]
[ 0 75 0]
[ 3 1 52]]
FOLD 7 Number of neighbors 23
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.93 0.96 0.95 55
avg / total 0.97 0.96 0.97 200
Confusion Matrix (Fold 7 ):
[[66 0 4]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 23 neighbours): 96.24% (+/- 1.29%)
FOLD 1 Number of neighbors 25
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.92 0.96 71
sport
1.00 1.00 1.00 76
technology
0.90 1.00 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[65 0 6]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 25
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.97 0.98 75
technology
0.93 1.00 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 1 3]
[ 1 73 1]
[ 0 0 56]]
FOLD 3 Number of neighbors 25
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 25
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.97 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 2 73 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 25
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.99 0.97 0.98 75
technology
0.93 1.00 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[65 1 4]
[ 2 73 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 25
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.96 0.91 0.93 70
sport
0.97 1.00 0.99 75
technology
0.91 0.93 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 6 ):
[[64 1 5]
[ 0 75 0]
[ 3 1 52]]
FOLD 7 Number of neighbors 25
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.96 0.99 0.97 75
technology
0.93 0.98 0.96 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[64 2 4]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 25 neighbours): 96.26% (+/- 1.26%)
FOLD 1 Number of neighbors 27
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.93 0.96 71
sport
1.00 1.00 1.00 76
technology
0.92 1.00 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[66 0 5]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 27
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.97 0.98 75
technology
0.93 1.00 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 1 3]
[ 1 73 1]
[ 0 0 56]]
FOLD 3 Number of neighbors 27
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 27
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 27
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[65 1 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 27
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.90 0.93 70
sport
0.95 1.00 0.97 75
technology
0.91 0.93 0.92 56
avg / total 0.95 0.95 0.94 201
Confusion Matrix (Fold 6 ):
[[63 2 5]
[ 0 75 0]
[ 2 2 52]]
FOLD 7 Number of neighbors 27
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.96 0.95 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 27 neighbours): 96.29% (+/- 1.24%)
FOLD 1 Number of neighbors 29
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.93 0.96 71
sport
1.00 1.00 1.00 76
technology
0.92 1.00 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[66 0 5]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 29
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.97 0.98 75
technology
0.93 1.00 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 1 3]
[ 1 73 1]
[ 0 0 56]]
FOLD 3 Number of neighbors 29
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 29
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 29
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.94 0.94 0.94 70
sport
0.99 0.97 0.98 75
technology
0.95 0.96 0.96 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 2 73 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 29
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.96 1.00 0.98 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[64 2 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 29
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 0.96 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 29 neighbours): 96.32% (+/- 1.23%)
FOLD 1 Number of neighbors 31
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.93 0.96 71
sport
1.00 1.00 1.00 76
technology
0.92 1.00 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[66 0 5]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 31
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.97 0.98 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 73 1]
[ 0 0 56]]
FOLD 3 Number of neighbors 31
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 31
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 2 0 54]]
FOLD 5 Number of neighbors 31
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 31
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.98 0.89 0.93 70
sport
0.96 1.00 0.98 75
technology
0.92 0.98 0.95 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[62 3 5]
[ 0 75 0]
[ 1 0 55]]
FOLD 7 Number of neighbors 31
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 31 neighbours): 96.35% (+/- 1.21%)
FOLD 1 Number of neighbors 33
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.93 0.96 71
sport
1.00 1.00 1.00 76
technology
0.92 1.00 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[66 0 5]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 33
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.97 0.98 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 73 1]
[ 0 0 56]]
FOLD 3 Number of neighbors 33
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 33
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 33
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 33
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.89 0.93 70
sport
0.95 1.00 0.97 75
technology
0.91 0.95 0.93 56
avg / total 0.95 0.95 0.94 201
Confusion Matrix (Fold 6 ):
[[62 3 5]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 33
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.96 0.95 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 33 neighbours): 96.39% (+/- 1.21%)
FOLD 1 Number of neighbors 35
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 35
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.96 0.97 75
technology
0.95 1.00 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 2 72 1]
[ 0 0 56]]
FOLD 3 Number of neighbors 35
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 35
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 35
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 35
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.97 1.00 0.99 75
technology
0.93 0.96 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[65 1 4]
[ 0 75 0]
[ 1 1 54]]
FOLD 7 Number of neighbors 35
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 35 neighbours): 96.43% (+/- 1.20%)
FOLD 1 Number of neighbors 37
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 37
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.97 0.98 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 73 1]
[ 0 0 56]]
FOLD 3 Number of neighbors 37
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 37
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 37
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 37
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.96 1.00 0.98 75
technology
0.93 0.96 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[64 2 4]
[ 0 75 0]
[ 1 1 54]]
FOLD 7 Number of neighbors 37
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.95 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 37 neighbours): 96.45% (+/- 1.19%)
FOLD 1 Number of neighbors 39
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 39
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.97 0.98 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 73 1]
[ 0 0 56]]
FOLD 3 Number of neighbors 39
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 39
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 39
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
1.00 0.97 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 2 73 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 39
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.92 0.96 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[64 1 5]
[ 0 75 0]
[ 1 1 54]]
FOLD 7 Number of neighbors 39
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.96 0.99 0.97 75
technology
0.95 0.98 0.96 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 39 neighbours): 96.49% (+/- 1.19%)
FOLD 1 Number of neighbors 41
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 41
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.97 0.98 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 73 1]
[ 0 0 56]]
FOLD 3 Number of neighbors 41
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 41
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 41
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
1.00 0.96 0.98 70
sport
1.00 1.00 1.00 75
technology
0.95 1.00 0.97 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 41
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.96 1.00 0.98 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 6 ):
[[64 2 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 41
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 41 neighbours): 96.52% (+/- 1.19%)
FOLD 1 Number of neighbors 43
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 43
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.97 0.98 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 73 1]
[ 0 0 56]]
FOLD 3 Number of neighbors 43
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 43
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 43
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 43
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.96 1.00 0.98 75
technology
0.93 0.96 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[64 2 4]
[ 0 75 0]
[ 1 1 54]]
FOLD 7 Number of neighbors 43
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 43 neighbours): 96.55% (+/- 1.18%)
FOLD 1 Number of neighbors 45
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 45
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.97 0.98 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 73 1]
[ 0 0 56]]
FOLD 3 Number of neighbors 45
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 45
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 45
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
1.00 0.96 0.98 70
sport
1.00 1.00 1.00 75
technology
0.95 1.00 0.97 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 45
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.97 1.00 0.99 75
technology
0.93 0.96 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[65 1 4]
[ 0 75 0]
[ 1 1 54]]
FOLD 7 Number of neighbors 45
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 45 neighbours): 96.58% (+/- 1.18%)
FOLD 1 Number of neighbors 47
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 47
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.97 0.98 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 73 1]
[ 0 0 56]]
FOLD 3 Number of neighbors 47
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 47
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[66 1 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 47
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 47
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 47
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.96 0.99 0.97 75
technology
0.95 0.98 0.96 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 47 neighbours): 96.60% (+/- 1.17%)
FOLD 1 Number of neighbors 49
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 49
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.97 0.98 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 73 1]
[ 0 0 56]]
FOLD 3 Number of neighbors 49
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 49
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 49
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 49
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 49
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 49 neighbours): 96.62% (+/- 1.16%)
FOLD 1 Number of neighbors 51
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
1.00 1.00 1.00 76
technology
0.95 1.00 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 51
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.97 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 3 Number of neighbors 51
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 51
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 51
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
1.00 0.96 0.98 70
sport
1.00 1.00 1.00 75
technology
0.95 1.00 0.97 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 51
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 51
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 51 neighbours): 96.65% (+/- 1.16%)
FOLD 1 Number of neighbors 53
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 53
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 3 Number of neighbors 53
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 53
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 53
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 53
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 53
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 53 neighbours): 96.67% (+/- 1.16%)
FOLD 1 Number of neighbors 55
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
1.00 1.00 1.00 76
technology
0.95 1.00 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 55
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.97 0.98 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 73 1]
[ 0 0 56]]
FOLD 3 Number of neighbors 55
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 55
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 55
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 55
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.99 1.00 0.99 75
technology
0.93 0.96 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[65 1 4]
[ 0 75 0]
[ 2 0 54]]
FOLD 7 Number of neighbors 55
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 55 neighbours): 96.69% (+/- 1.15%)
FOLD 1 Number of neighbors 57
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
1.00 1.00 1.00 76
technology
0.95 1.00 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 57
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.97 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 3 Number of neighbors 57
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.99 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 57
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 57
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 57
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 57
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.95 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 57 neighbours): 96.71% (+/- 1.15%)
FOLD 1 Number of neighbors 59
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 59
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.96 0.99 0.97 75
technology
0.96 0.98 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 0 1 55]]
FOLD 3 Number of neighbors 59
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 59
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 59
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
1.00 0.96 0.98 70
sport
1.00 1.00 1.00 75
technology
0.95 1.00 0.97 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 59
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 59
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.95 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 59 neighbours): 96.72% (+/- 1.15%)
FOLD 1 Number of neighbors 61
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 61
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.96 0.97 0.97 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 73 1]
[ 0 1 55]]
FOLD 3 Number of neighbors 61
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 61
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 61
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 61
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 7 Number of neighbors 61
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 61 neighbours): 96.73% (+/- 1.14%)
FOLD 1 Number of neighbors 63
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 63
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 0.97 0.97 75
technology
0.95 1.00 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 73 1]
[ 0 0 56]]
FOLD 3 Number of neighbors 63
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 63
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 63
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 63
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.97 1.00 0.99 75
technology
0.93 0.96 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[65 1 4]
[ 0 75 0]
[ 1 1 54]]
FOLD 7 Number of neighbors 63
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 63 neighbours): 96.74% (+/- 1.13%)
FOLD 1 Number of neighbors 65
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 65
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 0.97 0.97 75
technology
0.95 1.00 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 73 1]
[ 0 0 56]]
FOLD 3 Number of neighbors 65
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.90 0.94 70
sport
0.97 1.00 0.99 75
technology
0.92 0.98 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[63 2 5]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 65
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[66 1 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 65
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 65
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 1 54]]
FOLD 7 Number of neighbors 65
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 65 neighbours): 96.75% (+/- 1.12%)
FOLD 1 Number of neighbors 67
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 67
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 0.97 0.97 75
technology
0.95 1.00 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 73 1]
[ 0 0 56]]
FOLD 3 Number of neighbors 67
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 67
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[66 1 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 67
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 67
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 1 54]]
FOLD 7 Number of neighbors 67
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 0.99 0.98 75
technology
0.93 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 1 4]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 67 neighbours): 96.76% (+/- 1.11%)
FOLD 1 Number of neighbors 69
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
1.00 1.00 1.00 76
technology
0.95 1.00 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 69
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.97 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 3 Number of neighbors 69
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 69
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 69
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 69
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 1 54]]
FOLD 7 Number of neighbors 69
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.96 0.99 0.97 75
technology
0.93 0.96 0.95 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[64 2 4]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 69 neighbours): 96.78% (+/- 1.11%)
FOLD 1 Number of neighbors 71
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 71
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.97 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 3 Number of neighbors 71
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 71
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 71
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 71
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 1 55]]
FOLD 7 Number of neighbors 71
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 0.99 0.98 75
technology
0.93 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 1 4]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 71 neighbours): 96.79% (+/- 1.11%)
FOLD 1 Number of neighbors 73
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 73
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 73
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 73
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 73
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 73
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.93 0.96 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[65 1 4]
[ 0 75 0]
[ 0 1 55]]
FOLD 7 Number of neighbors 73
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.96 0.99 0.97 75
technology
0.93 0.96 0.95 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[64 2 4]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 73 neighbours): 96.80% (+/- 1.10%)
FOLD 1 Number of neighbors 75
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
0.99 1.00 0.99 76
technology
0.93 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 75
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 75
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.90 0.94 70
sport
0.97 1.00 0.99 75
technology
0.92 0.98 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[63 2 5]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 75
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[66 1 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 75
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 75
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.93 0.96 70
sport
0.99 1.00 0.99 75
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[65 1 4]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 75
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.96 0.99 0.97 75
technology
0.95 0.98 0.96 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 75 neighbours): 96.80% (+/- 1.09%)
FOLD 1 Number of neighbors 77
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
1.00 1.00 1.00 76
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 0 56]]
FOLD 2 Number of neighbors 77
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.96 0.97 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 2 73 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 77
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 77
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 77
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 77
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 1 55]]
FOLD 7 Number of neighbors 77
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.98 0.96 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 77 neighbours): 96.81% (+/- 1.08%)
FOLD 1 Number of neighbors 79
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.94 0.97 71
sport
0.99 1.00 0.99 76
technology
0.93 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[67 0 4]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 79
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.97 0.97 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 2 73 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 79
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.97 1.00 0.99 75
technology
0.93 0.96 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 79
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 79
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 79
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 1 54]]
FOLD 7 Number of neighbors 79
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.98 0.96 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 79 neighbours): 96.82% (+/- 1.08%)
FOLD 1 Number of neighbors 81
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
0.99 1.00 0.99 76
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 81
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.96 0.97 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 2 73 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 81
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 81
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 81
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 81
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 1 54]]
FOLD 7 Number of neighbors 81
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.96 0.99 0.97 75
technology
0.95 0.98 0.96 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 81 neighbours): 96.83% (+/- 1.07%)
FOLD 1 Number of neighbors 83
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
0.99 1.00 0.99 76
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 83
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.97 0.96 75
technology
0.96 0.96 0.96 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 83
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
1.00 0.91 0.96 70
sport
0.97 1.00 0.99 75
technology
0.93 1.00 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 0 0 56]]
FOLD 4 Number of neighbors 83
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 83
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 83
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.93 0.96 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[65 1 4]
[ 0 75 0]
[ 0 1 55]]
FOLD 7 Number of neighbors 83
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.96 0.99 0.97 75
technology
0.95 0.98 0.96 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 83 neighbours): 96.84% (+/- 1.06%)
FOLD 1 Number of neighbors 85
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
0.99 1.00 0.99 76
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 85
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 85
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 85
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 85
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 85
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 1 55]]
FOLD 7 Number of neighbors 85
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.96 0.99 0.97 75
technology
0.95 0.98 0.96 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 85 neighbours): 96.85% (+/- 1.06%)
FOLD 1 Number of neighbors 87
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
0.99 1.00 0.99 76
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 87
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.96 0.95 75
technology
0.96 0.96 0.96 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 3 72 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 87
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.97 1.00 0.99 75
technology
0.93 0.96 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 87
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 87
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 87
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 1 54]]
FOLD 7 Number of neighbors 87
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.98 0.96 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 87 neighbours): 96.85% (+/- 1.05%)
FOLD 1 Number of neighbors 89
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
0.99 1.00 0.99 76
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 89
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.97 0.96 75
technology
0.96 0.96 0.96 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 89
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.97 1.00 0.99 75
technology
0.93 0.96 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 89
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 89
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 89
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 1 55]]
FOLD 7 Number of neighbors 89
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.96 0.99 0.97 75
technology
0.95 0.98 0.96 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 89 neighbours): 96.86% (+/- 1.05%)
FOLD 1 Number of neighbors 91
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.96 0.98 71
sport
0.99 1.00 0.99 76
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[68 0 3]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 91
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 91
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 91
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 91
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 91
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.97 1.00 0.99 75
technology
0.93 0.96 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[65 1 4]
[ 0 75 0]
[ 1 1 54]]
FOLD 7 Number of neighbors 91
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.98 0.96 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 91 neighbours): 96.86% (+/- 1.04%)
FOLD 1 Number of neighbors 93
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 93
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.97 0.96 75
technology
0.96 0.96 0.96 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 93
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.97 1.00 0.99 75
technology
0.93 0.96 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 93
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 93
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 93
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 1 55]]
FOLD 7 Number of neighbors 93
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.97 0.99 0.98 75
technology
0.93 0.98 0.96 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 1 4]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 93 neighbours): 96.87% (+/- 1.04%)
FOLD 1 Number of neighbors 95
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 95
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.96 0.96 0.96 75
technology
0.95 0.96 0.96 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 2 72 1]
[ 0 2 54]]
FOLD 3 Number of neighbors 95
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.90 0.94 70
sport
0.97 1.00 0.99 75
technology
0.92 0.98 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[63 2 5]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 95
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 95
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 95
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 7 Number of neighbors 95
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.96 0.99 0.97 75
technology
0.93 0.98 0.96 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[64 2 4]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 95 neighbours): 96.87% (+/- 1.04%)
FOLD 1 Number of neighbors 97
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 97
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.95 0.95 75
technology
0.95 0.96 0.96 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 3 71 1]
[ 0 2 54]]
FOLD 3 Number of neighbors 97
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 97
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 97
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 97
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 1 55]]
FOLD 7 Number of neighbors 97
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.98 0.96 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 97 neighbours): 96.88% (+/- 1.04%)
FOLD 1 Number of neighbors 99
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 99
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 99
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.90 0.93 70
sport
0.97 1.00 0.99 75
technology
0.92 0.96 0.94 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[63 2 5]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 99
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 99
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 99
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.93 0.96 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[65 1 4]
[ 0 75 0]
[ 0 1 55]]
FOLD 7 Number of neighbors 99
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.96 0.99 0.97 75
technology
0.95 0.98 0.96 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 99 neighbours): 96.88% (+/- 1.04%)
FOLD 1 Number of neighbors 101
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 101
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 101
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.99 1.00 0.99 75
technology
0.92 0.98 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[64 1 5]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 101
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 101
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 101
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 1 54]]
FOLD 7 Number of neighbors 101
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.96 0.99 0.97 75
technology
0.93 0.98 0.96 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[64 2 4]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 101 neighbours): 96.89% (+/- 1.04%)
FOLD 1 Number of neighbors 103
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 103
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 103
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
1.00 0.91 0.96 70
sport
0.97 1.00 0.99 75
technology
0.93 1.00 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 0 0 56]]
FOLD 4 Number of neighbors 103
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 103
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 103
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.97 1.00 0.99 75
technology
0.93 0.96 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[65 1 4]
[ 0 75 0]
[ 1 1 54]]
FOLD 7 Number of neighbors 103
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.98 0.96 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 103 neighbours): 96.90% (+/- 1.03%)
FOLD 1 Number of neighbors 105
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 105
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 105
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.90 0.94 70
sport
0.97 1.00 0.99 75
technology
0.92 0.98 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[63 2 5]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 105
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 105
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 105
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.93 0.96 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[65 1 4]
[ 0 75 0]
[ 0 1 55]]
FOLD 7 Number of neighbors 105
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.97 0.99 0.98 75
technology
0.93 0.98 0.96 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 1 4]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 105 neighbours): 96.90% (+/- 1.03%)
FOLD 1 Number of neighbors 107
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 107
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.96 0.97 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 2 73 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 107
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.90 0.94 70
sport
0.97 1.00 0.99 75
technology
0.92 0.98 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[63 2 5]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 107
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 107
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 107
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 1 55]]
FOLD 7 Number of neighbors 107
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.98 0.96 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 107 neighbours): 96.91% (+/- 1.03%)
FOLD 1 Number of neighbors 109
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 109
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 109
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
1.00 0.90 0.95 70
sport
0.96 1.00 0.98 75
technology
0.92 0.98 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[63 2 5]
[ 0 75 0]
[ 0 1 55]]
FOLD 4 Number of neighbors 109
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 109
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 109
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 1 55]]
FOLD 7 Number of neighbors 109
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.96 0.99 0.97 75
technology
0.95 0.98 0.96 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 2 3]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 109 neighbours): 96.91% (+/- 1.03%)
FOLD 1 Number of neighbors 111
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 111
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 111
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.91 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 1 0 55]]
FOLD 4 Number of neighbors 111
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 111
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 111
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 111
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.97 0.99 0.98 75
technology
0.93 0.98 0.96 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[65 1 4]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 111 neighbours): 96.92% (+/- 1.03%)
FOLD 1 Number of neighbors 113
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 113
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 113
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.90 0.93 70
sport
0.97 1.00 0.99 75
technology
0.92 0.96 0.94 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[63 2 5]
[ 0 75 0]
[ 2 0 54]]
FOLD 4 Number of neighbors 113
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 113
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 113
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.93 0.96 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[65 1 4]
[ 0 75 0]
[ 0 1 55]]
FOLD 7 Number of neighbors 113
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.98 0.96 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 113 neighbours): 96.92% (+/- 1.03%)
FOLD 1 Number of neighbors 115
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 115
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 115
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
1.00 0.91 0.96 70
sport
0.96 1.00 0.98 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 0 1 55]]
FOLD 4 Number of neighbors 115
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 115
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 115
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.93 0.96 70
sport
0.99 1.00 0.99 75
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[65 1 4]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 115
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.98 0.96 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 115 neighbours): 96.93% (+/- 1.03%)
FOLD 1 Number of neighbors 117
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 117
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 117
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.97 1.00 0.99 75
technology
0.93 0.96 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 1 1 54]]
FOLD 4 Number of neighbors 117
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 117
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 117
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
1.00 1.00 1.00 75
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 0 4]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 117
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 0.96 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 117 neighbours): 96.94% (+/- 1.03%)
FOLD 1 Number of neighbors 119
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 119
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.96 0.96 0.96 75
technology
0.96 0.96 0.96 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 3 72 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 119
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.96 1.00 0.98 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 1 1 54]]
FOLD 4 Number of neighbors 119
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 119
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 119
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.93 0.96 70
sport
0.99 1.00 0.99 75
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[65 1 4]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 119
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.96 0.95 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 119 neighbours): 96.94% (+/- 1.02%)
FOLD 1 Number of neighbors 121
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 121
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.97 0.96 75
technology
0.96 0.96 0.96 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 121
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
1.00 0.91 0.96 70
sport
0.97 1.00 0.99 75
technology
0.92 0.98 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[64 1 5]
[ 0 75 0]
[ 0 1 55]]
FOLD 4 Number of neighbors 121
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[66 1 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 121
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 121
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 121
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.98 0.96 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 121 neighbours): 96.95% (+/- 1.02%)
FOLD 1 Number of neighbors 123
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 123
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.96 0.95 75
technology
0.96 0.96 0.96 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 3 72 0]
[ 0 2 54]]
FOLD 3 Number of neighbors 123
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.96 1.00 0.98 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 123
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.99 0.97 0.98 75
technology
0.95 1.00 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[66 1 3]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 123
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 123
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 123
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.98 0.96 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 123 neighbours): 96.95% (+/- 1.02%)
FOLD 1 Number of neighbors 125
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 125
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 0.97 0.97 75
technology
0.96 0.98 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 0 1 55]]
FOLD 3 Number of neighbors 125
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.90 0.94 70
sport
0.96 1.00 0.98 75
technology
0.92 0.96 0.94 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[63 2 5]
[ 0 75 0]
[ 1 1 54]]
FOLD 4 Number of neighbors 125
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.99 0.97 0.98 75
technology
0.95 1.00 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[66 1 3]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 125
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 125
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.93 0.96 70
sport
0.99 1.00 0.99 75
technology
0.93 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[65 1 4]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 125
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.98 0.96 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 125 neighbours): 96.95% (+/- 1.02%)
FOLD 1 Number of neighbors 127
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 127
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 0.97 0.97 75
technology
0.96 0.98 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 0 1 55]]
FOLD 3 Number of neighbors 127
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 127
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
1.00 0.97 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 0 3]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 127
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 127
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 1 55]]
FOLD 7 Number of neighbors 127
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.99 0.99 75
technology
0.95 0.96 0.95 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 127 neighbours): 96.96% (+/- 1.02%)
FOLD 1 Number of neighbors 129
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 129
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.94 0.94 70
sport
0.96 0.96 0.96 75
technology
0.96 0.96 0.96 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 3 72 0]
[ 1 1 54]]
FOLD 3 Number of neighbors 129
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.98 0.90 0.94 70
sport
0.96 1.00 0.98 75
technology
0.92 0.96 0.94 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[63 2 5]
[ 0 75 0]
[ 1 1 54]]
FOLD 4 Number of neighbors 129
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.97 0.98 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 129
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 129
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 129
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.98 0.96 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 129 neighbours): 96.96% (+/- 1.02%)
FOLD 1 Number of neighbors 131
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 131
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.96 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 131
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
1.00 0.90 0.95 70
sport
0.97 1.00 0.99 75
technology
0.92 1.00 0.96 56
avg / total 0.97 0.97 0.96 201
Confusion Matrix (Fold 3 ):
[[63 2 5]
[ 0 75 0]
[ 0 0 56]]
FOLD 4 Number of neighbors 131
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.99 0.97 0.98 75
technology
0.95 1.00 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[66 1 3]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 131
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 131
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 131
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.98 0.96 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 0 1 54]]
Model accuracy (for 131 neighbours): 96.96% (+/- 1.02%)
FOLD 1 Number of neighbors 133
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 133
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 0.99 0.98 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 74 0]
[ 0 1 55]]
FOLD 3 Number of neighbors 133
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 1 1 54]]
FOLD 4 Number of neighbors 133
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 133
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 133
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.93 0.96 70
sport
0.97 1.00 0.99 75
technology
0.93 0.98 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[65 1 4]
[ 0 75 0]
[ 0 1 55]]
FOLD 7 Number of neighbors 133
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.96 0.95 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 133 neighbours): 96.97% (+/- 1.01%)
FOLD 1 Number of neighbors 135
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 135
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.96 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 135
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 135
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
1.00 0.97 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 0 3]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 135
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 135
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 135
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.96 0.95 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (for 135 neighbours): 96.97% (+/- 1.01%)
FOLD 1 Number of neighbors 137
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 137
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.94 0.94 70
sport
0.96 0.96 0.96 75
technology
0.96 0.96 0.96 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 3 72 0]
[ 1 1 54]]
FOLD 3 Number of neighbors 137
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 137
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.99 0.97 0.98 75
technology
0.95 1.00 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[66 1 3]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 137
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 137
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 137
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 137 neighbours): 96.98% (+/- 1.01%)
FOLD 1 Number of neighbors 139
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 139
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 139
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 139
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
1.00 0.97 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 0 3]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 139
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 139
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 139
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 139 neighbours): 96.98% (+/- 1.01%)
FOLD 1 Number of neighbors 141
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 141
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.96 0.99 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 141
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 141
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
1.00 0.97 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 0 3]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 141
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 141
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 141
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 141 neighbours): 96.98% (+/- 1.01%)
FOLD 1 Number of neighbors 143
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 143
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.95 0.97 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[66 2 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 143
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 143
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 143
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 143
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 1 55]]
FOLD 7 Number of neighbors 143
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 143 neighbours): 96.98% (+/- 1.01%)
FOLD 1 Number of neighbors 145
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 145
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.96 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 145
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.90 0.93 70
sport
0.96 1.00 0.98 75
technology
0.91 0.95 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 3 ):
[[63 2 5]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 145
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.97 0.97 75
technology
0.96 0.98 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 145
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 145
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 145
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 145 neighbours): 96.98% (+/- 1.02%)
FOLD 1 Number of neighbors 147
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 147
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.96 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 147
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.96 1.00 0.98 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 147
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.99 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 0 3]
[ 1 74 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 147
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 147
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 147
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 147 neighbours): 96.98% (+/- 1.02%)
FOLD 1 Number of neighbors 149
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 149
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 149
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.90 0.93 70
sport
0.96 1.00 0.98 75
technology
0.91 0.95 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 3 ):
[[63 2 5]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 149
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.97 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 149
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 149
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 149
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 149 neighbours): 96.99% (+/- 1.02%)
FOLD 1 Number of neighbors 151
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 151
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.96 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 151
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.96 1.00 0.98 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 151
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.99 0.97 0.98 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 151
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 151
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 151
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 0.99 0.97 75
technology
0.93 0.95 0.94 55
avg / total 0.96 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[65 1 4]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 151 neighbours): 96.99% (+/- 1.02%)
FOLD 1 Number of neighbors 153
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 153
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.97 0.99 0.98 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 1 74 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 153
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.96 1.00 0.98 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 153
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.97 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 153
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 153
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 153
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 153 neighbours): 96.99% (+/- 1.02%)
FOLD 1 Number of neighbors 155
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 155
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.96 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 155
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.90 0.93 70
sport
0.96 1.00 0.98 75
technology
0.91 0.95 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 3 ):
[[63 2 5]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 155
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
1.00 0.97 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[67 0 3]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 155
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 155
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 155
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 155 neighbours): 96.99% (+/- 1.02%)
FOLD 1 Number of neighbors 157
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.99 0.97 0.98 76
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 157
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 157
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 157
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.97 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 157
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 157
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.97 1.00 0.99 75
technology
0.93 0.96 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 0 4]
[ 0 75 0]
[ 0 2 54]]
FOLD 7 Number of neighbors 157
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 157 neighbours): 96.99% (+/- 1.02%)
FOLD 1 Number of neighbors 159
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 159
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 159
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.90 0.93 70
sport
0.96 1.00 0.98 75
technology
0.91 0.95 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 3 ):
[[63 2 5]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 159
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 159
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 159
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.95 1.00 0.97 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 3 53]]
FOLD 7 Number of neighbors 159
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 159 neighbours): 96.99% (+/- 1.02%)
FOLD 1 Number of neighbors 161
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.97 1.00 0.99 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 161
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 161
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.97 1.00 0.99 75
technology
0.91 0.95 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[64 1 5]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 161
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.97 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 161
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 161
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 161
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 161 neighbours): 96.99% (+/- 1.02%)
FOLD 1 Number of neighbors 163
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.99 0.99 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 163
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 163
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 163
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 163
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 163
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 163
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 0.99 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 1 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 163 neighbours): 97.00% (+/- 1.02%)
FOLD 1 Number of neighbors 165
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 165
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 165
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 165
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
1.00 0.97 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 2 73 0]
[ 1 0 55]]
FOLD 5 Number of neighbors 165
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 165
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 165
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 165 neighbours): 97.00% (+/- 1.02%)
FOLD 1 Number of neighbors 167
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 167
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 167
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 167
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 167
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 167
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.95 1.00 0.97 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 3 53]]
FOLD 7 Number of neighbors 167
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 167 neighbours): 97.00% (+/- 1.02%)
FOLD 1 Number of neighbors 169
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.97 0.97 0.97 76
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 169
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 169
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 169
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.99 0.97 0.98 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 169
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 169
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 169
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 169 neighbours): 97.00% (+/- 1.02%)
FOLD 1 Number of neighbors 171
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 171
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.96 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 171
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 171
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.97 0.97 75
technology
0.96 0.98 0.97 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 4 ):
[[67 1 2]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 171
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 171
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.96 0.98 70
sport
1.00 1.00 1.00 75
technology
0.95 1.00 0.97 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 171
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 171 neighbours): 97.01% (+/- 1.02%)
FOLD 1 Number of neighbors 173
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.97 0.99 0.98 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 173
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 173
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 173
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.99 0.97 0.98 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 173
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 173
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.96 0.98 70
sport
0.99 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 0 1 55]]
FOLD 7 Number of neighbors 173
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 173 neighbours): 97.01% (+/- 1.01%)
FOLD 1 Number of neighbors 175
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 175
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 175
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 175
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 175
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 2 0 54]]
FOLD 6 Number of neighbors 175
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 175
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 175 neighbours): 97.01% (+/- 1.01%)
FOLD 1 Number of neighbors 177
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.99 0.99 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 177
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 177
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 2 52]]
FOLD 4 Number of neighbors 177
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.99 0.97 0.98 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 177
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 177
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.96 0.98 70
sport
1.00 1.00 1.00 75
technology
0.95 1.00 0.97 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 177
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 177 neighbours): 97.01% (+/- 1.01%)
FOLD 1 Number of neighbors 179
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 179
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 179
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.95 1.00 0.97 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[65 2 3]
[ 0 75 0]
[ 2 2 52]]
FOLD 4 Number of neighbors 179
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 179
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 179
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 179
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 179 neighbours): 97.01% (+/- 1.01%)
FOLD 1 Number of neighbors 181
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 181
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 181
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 181
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.99 0.97 0.98 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 181
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 181
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 181
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 181 neighbours): 97.02% (+/- 1.01%)
FOLD 1 Number of neighbors 183
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.97 1.00 0.99 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 183
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.93 0.97 0.95 70
sport
0.97 0.95 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 4 71 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 183
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 183
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.99 0.97 0.98 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 183
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 183
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 183
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 183 neighbours): 97.02% (+/- 1.01%)
FOLD 1 Number of neighbors 185
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.97 0.99 0.98 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 185
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 185
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.90 0.93 70
sport
0.96 1.00 0.98 75
technology
0.91 0.95 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 3 ):
[[63 2 5]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 185
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 185
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 185
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.96 0.98 70
sport
1.00 1.00 1.00 75
technology
0.95 1.00 0.97 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 185
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 185 neighbours): 97.02% (+/- 1.01%)
FOLD 1 Number of neighbors 187
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.97 0.97 0.97 76
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 187
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.96 0.95 70
sport
0.96 0.96 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[67 1 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 187
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 187
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 187
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 187
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 187
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 187 neighbours): 97.02% (+/- 1.01%)
FOLD 1 Number of neighbors 189
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.97 0.99 0.98 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 189
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 189
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 1 74 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 189
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
0.99 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[68 0 2]
[ 1 74 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 189
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 189
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.96 0.98 70
sport
0.97 1.00 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 0 2 54]]
FOLD 7 Number of neighbors 189
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 189 neighbours): 97.02% (+/- 1.01%)
FOLD 1 Number of neighbors 191
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.97 0.99 0.98 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 191
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 191
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 191
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.97 0.98 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 191
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 191
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 191
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 191 neighbours): 97.02% (+/- 1.00%)
FOLD 1 Number of neighbors 193
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.97 0.97 0.97 76
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 193
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 193
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 193
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.97 0.98 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 193
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 193
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.96 1.00 0.98 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 2 54]]
FOLD 7 Number of neighbors 193
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 193 neighbours): 97.02% (+/- 1.00%)
FOLD 1 Number of neighbors 195
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.99 0.99 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 195
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 195
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.97 1.00 0.99 75
technology
0.91 0.95 0.93 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[64 1 5]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 195
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.99 0.99 70
sport
0.99 0.99 0.99 75
technology
0.98 0.98 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 1 74 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 195
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 195
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.96 0.98 70
sport
0.97 1.00 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 0 2 54]]
FOLD 7 Number of neighbors 195
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 195 neighbours): 97.02% (+/- 1.00%)
FOLD 1 Number of neighbors 197
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.97 0.99 0.98 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 197
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 197
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 197
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.99 0.99 70
sport
0.99 0.99 0.99 75
technology
0.98 0.98 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 1 74 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 197
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 197
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.96 0.98 70
sport
0.97 1.00 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 0 2 54]]
FOLD 7 Number of neighbors 197
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 197 neighbours): 97.02% (+/- 1.00%)
FOLD 1 Number of neighbors 199
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.99 1.00 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.99 0.99 0.99 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 199
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 199
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.96 1.00 0.98 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 199
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
1.00 0.97 0.99 75
technology
0.98 1.00 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 199
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 199
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.96 1.00 0.98 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 2 54]]
FOLD 7 Number of neighbors 199
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 199 neighbours): 97.02% (+/- 1.00%)
FOLD 1 Number of neighbors 201
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.99 0.97 0.98 76
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 201
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 201
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 201
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.99 0.97 0.98 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 201
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 201
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.96 0.98 70
sport
1.00 1.00 1.00 75
technology
0.95 1.00 0.97 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 201
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 201 neighbours): 97.02% (+/- 1.00%)
FOLD 1 Number of neighbors 203
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.97 1.00 0.99 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 203
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 203
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.96 1.00 0.98 75
technology
0.91 0.93 0.92 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 3 ):
[[64 1 5]
[ 0 75 0]
[ 2 2 52]]
FOLD 4 Number of neighbors 203
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.97 0.98 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 203
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 203
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.96 0.98 70
sport
0.97 1.00 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 0 2 54]]
FOLD 7 Number of neighbors 203
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.97 0.97 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 2 73 0]
[ 1 2 52]]
Model accuracy (for 203 neighbours): 97.02% (+/- 1.00%)
FOLD 1 Number of neighbors 205
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.97 1.00 0.99 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 205
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.97 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 2 73 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 205
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 205
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.97 0.98 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 205
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 205
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 205
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 205 neighbours): 97.03% (+/- 1.00%)
FOLD 1 Number of neighbors 207
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.97 0.97 0.97 76
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 207
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 207
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 207
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
1.00 0.97 0.99 75
technology
0.98 1.00 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 207
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 207
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.96 0.98 70
sport
1.00 1.00 1.00 75
technology
0.95 1.00 0.97 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 207
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 207 neighbours): 97.03% (+/- 1.00%)
FOLD 1 Number of neighbors 209
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.96 1.00 0.98 76
technology
0.96 0.95 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 209
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 209
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 209
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.99 0.99 70
sport
0.99 0.99 0.99 75
technology
0.98 0.98 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 1 74 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 209
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 209
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 209
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.97 0.97 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 2 73 0]
[ 1 2 52]]
Model accuracy (for 209 neighbours): 97.03% (+/- 1.00%)
FOLD 1 Number of neighbors 211
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.97 0.99 0.98 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 211
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 211
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 211
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.99 0.99 70
sport
1.00 0.99 0.99 75
technology
0.98 1.00 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 211
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 211
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.96 0.98 70
sport
1.00 1.00 1.00 75
technology
0.95 1.00 0.97 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 211
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 211 neighbours): 97.03% (+/- 1.00%)
FOLD 1 Number of neighbors 213
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.99 0.99 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 213
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 213
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 213
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
1.00 0.97 0.99 75
technology
0.98 1.00 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 213
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
1.00 0.99 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 213
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.96 0.98 70
sport
0.97 1.00 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 0 2 54]]
FOLD 7 Number of neighbors 213
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 213 neighbours): 97.03% (+/- 1.00%)
FOLD 1 Number of neighbors 215
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.99 0.99 0.99 76
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 215
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.93 0.97 0.95 70
sport
0.99 0.95 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 4 71 0]
[ 1 1 54]]
FOLD 3 Number of neighbors 215
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 1 74 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 215
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.96 0.99 0.97 70
sport
0.99 0.97 0.98 75
technology
0.98 0.96 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 1 1 54]]
FOLD 5 Number of neighbors 215
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 215
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 215
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 215 neighbours): 97.03% (+/- 1.00%)
FOLD 1 Number of neighbors 217
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.96 0.97 0.97 76
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 217
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 217
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 1 74 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 217
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.99 0.99 70
sport
0.99 0.99 0.99 75
technology
0.98 0.98 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 1 74 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 217
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 217
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.96 0.98 70
sport
1.00 1.00 1.00 75
technology
0.95 1.00 0.97 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 217
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 217 neighbours): 97.03% (+/- 1.00%)
FOLD 1 Number of neighbors 219
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.97 0.97 0.97 76
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 219
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.99 0.96 0.97 75
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 1 54]]
FOLD 3 Number of neighbors 219
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 219
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.97 0.98 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 219
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 219
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.99 1.00 0.99 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 0 55]]
FOLD 7 Number of neighbors 219
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.96 0.97 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.95 0.95 0.95 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 2 73 0]
[ 1 3 51]]
Model accuracy (for 219 neighbours): 97.03% (+/- 1.00%)
FOLD 1 Number of neighbors 221
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.99 0.97 0.98 76
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 1 55]]
FOLD 2 Number of neighbors 221
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.93 0.97 0.95 70
sport
0.97 0.95 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 4 71 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 221
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 221
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
1.00 0.97 0.99 75
technology
0.98 1.00 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 221
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 221
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.99 1.00 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 221
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.96 0.99 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 3 51]]
Model accuracy (for 221 neighbours): 97.03% (+/- 1.00%)
FOLD 1 Number of neighbors 223
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.97 0.97 0.97 76
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 223
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 223
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 223
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.97 0.98 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 223
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 223
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.96 1.00 0.98 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 2 54]]
FOLD 7 Number of neighbors 223
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 223 neighbours): 97.03% (+/- 1.00%)
FOLD 1 Number of neighbors 225
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.97 0.97 0.97 76
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 225
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.93 0.96 0.94 70
sport
0.97 0.95 0.96 75
technology
0.95 0.95 0.95 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 2 ):
[[67 0 3]
[ 4 71 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 225
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 225
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.99 0.99 70
sport
0.99 0.99 0.99 75
technology
0.98 0.98 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 1 74 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 225
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 225
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.93 0.96 70
sport
0.96 1.00 0.98 75
technology
0.93 0.96 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[65 1 4]
[ 0 75 0]
[ 0 2 54]]
FOLD 7 Number of neighbors 225
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 225 neighbours): 97.03% (+/- 1.00%)
FOLD 1 Number of neighbors 227
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.97 0.99 0.98 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 227
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 227
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 227
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.97 0.98 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 227
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 227
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 227
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 227 neighbours): 97.03% (+/- 1.00%)
FOLD 1 Number of neighbors 229
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.96 0.97 0.97 76
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 229
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 229
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 2 52]]
FOLD 4 Number of neighbors 229
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.99 0.99 70
sport
0.99 0.99 0.99 75
technology
0.98 0.98 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 1 74 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 229
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 229
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 229
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.97 0.97 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 2 73 0]
[ 1 2 52]]
Model accuracy (for 229 neighbours): 97.03% (+/- 1.00%)
FOLD 1 Number of neighbors 231
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.97 0.99 0.98 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 231
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 231
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.91 0.94 70
sport
0.96 1.00 0.98 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[64 2 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 231
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.99 0.99 70
sport
0.99 0.99 0.99 75
technology
0.98 0.98 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 1 74 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 231
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 231
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.96 0.98 70
sport
1.00 1.00 1.00 75
technology
0.95 1.00 0.97 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 0 0 56]]
FOLD 7 Number of neighbors 231
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.96 0.99 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 3 51]]
Model accuracy (for 231 neighbours): 97.03% (+/- 1.00%)
FOLD 1 Number of neighbors 233
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.97 0.97 0.97 76
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 233
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 233
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 233
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.97 0.98 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 233
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 233
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.96 1.00 0.98 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 2 54]]
FOLD 7 Number of neighbors 233
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 233 neighbours): 97.03% (+/- 1.00%)
FOLD 1 Number of neighbors 235
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.97 0.99 0.98 76
technology
0.96 0.96 0.96 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 235
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 235
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.97 0.99 0.98 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 1 74 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 235
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
1.00 0.97 0.99 75
technology
0.98 1.00 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 235
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 235
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.96 0.98 70
sport
0.97 1.00 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 0 2 54]]
FOLD 7 Number of neighbors 235
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 235 neighbours): 97.03% (+/- 1.00%)
FOLD 1 Number of neighbors 237
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.96 1.00 0.98 76
technology
0.96 0.95 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 237
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 237
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.96 0.99 0.97 75
technology
0.93 0.93 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 1 74 0]
[ 2 2 52]]
FOLD 4 Number of neighbors 237
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 0.99 0.99 70
sport
1.00 0.99 0.99 75
technology
0.98 1.00 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 237
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 237
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.96 1.00 0.98 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 2 54]]
FOLD 7 Number of neighbors 237
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 237 neighbours): 97.03% (+/- 1.01%)
FOLD 1 Number of neighbors 239
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.97 0.97 0.97 76
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 239
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.93 0.97 0.95 70
sport
0.97 0.95 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 4 71 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 239
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 239
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.97 0.98 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 239
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 239
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 239
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 239 neighbours): 97.03% (+/- 1.01%)
FOLD 1 Number of neighbors 241
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.96 0.97 0.97 76
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 241
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 241
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 241
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
1.00 0.97 0.99 75
technology
0.98 1.00 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 241
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 241
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 241
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 241 neighbours): 97.03% (+/- 1.01%)
FOLD 1 Number of neighbors 243
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.97 0.97 0.97 76
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 243
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.93 0.97 0.95 70
sport
0.97 0.95 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 4 71 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 243
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 243
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
1.00 0.97 0.99 75
technology
0.98 1.00 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 243
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 243
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 243
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.96 0.99 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 3 51]]
Model accuracy (for 243 neighbours): 97.03% (+/- 1.01%)
FOLD 1 Number of neighbors 245
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.96 0.97 0.97 76
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 245
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 245
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 245
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.97 0.98 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 245
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 245
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.96 1.00 0.98 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[65 1 4]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 245
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.96 0.99 0.97 75
technology
0.94 0.93 0.94 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 3 51]]
Model accuracy (for 245 neighbours): 97.03% (+/- 1.01%)
FOLD 1 Number of neighbors 247
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.96 1.00 0.98 76
technology
0.96 0.95 0.95 56
avg / total 0.98 0.98 0.98 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 76 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 247
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.93 0.97 0.95 70
sport
0.97 0.95 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 4 71 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 247
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 247
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.97 0.98 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 247
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 247
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.96 0.98 70
sport
0.97 1.00 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 0 2 54]]
FOLD 7 Number of neighbors 247
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 247 neighbours): 97.03% (+/- 1.00%)
FOLD 1 Number of neighbors 249
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.96 0.97 0.97 76
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 249
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 249
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 249
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
1.00 0.97 0.99 75
technology
0.98 1.00 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 249
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 249
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.96 1.00 0.98 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 2 54]]
FOLD 7 Number of neighbors 249
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.93 0.95 0.94 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 0 4]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 249 neighbours): 97.03% (+/- 1.01%)
FOLD 1 Number of neighbors 251
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.97 0.97 0.97 76
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 251
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 251
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 251
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.97 0.98 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 251
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
1.00 0.99 0.99 75
technology
0.95 1.00 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[67 0 3]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 251
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.96 1.00 0.98 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 2 54]]
FOLD 7 Number of neighbors 251
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 251 neighbours): 97.03% (+/- 1.00%)
FOLD 1 Number of neighbors 253
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.96 0.99 0.97 76
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 253
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.93 0.97 0.95 70
sport
0.97 0.95 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 4 71 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 253
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 1 74 0]
[ 2 2 52]]
FOLD 4 Number of neighbors 253
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.97 0.98 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 253
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 253
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.96 0.98 70
sport
0.97 1.00 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 0 2 54]]
FOLD 7 Number of neighbors 253
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.97 0.97 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 2 73 0]
[ 1 2 52]]
Model accuracy (for 253 neighbours): 97.03% (+/- 1.00%)
FOLD 1 Number of neighbors 255
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.96 0.99 0.97 76
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 255
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 255
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 255
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
1.00 0.97 0.99 75
technology
0.98 1.00 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 255
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 255
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.96 1.00 0.98 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 2 54]]
FOLD 7 Number of neighbors 255
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 255 neighbours): 97.03% (+/- 1.00%)
FOLD 1 Number of neighbors 257
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.96 0.97 0.97 76
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 257
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.93 0.97 0.95 70
sport
0.97 0.95 0.96 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 4 71 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 257
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 1 74 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 257
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.97 0.98 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 257
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 257
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 257
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 257 neighbours): 97.03% (+/- 1.00%)
FOLD 1 Number of neighbors 259
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.96 0.99 0.97 76
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 259
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 259
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 259
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
1.00 0.97 0.99 75
technology
0.98 1.00 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 259
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 259
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 259
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 259 neighbours): 97.03% (+/- 1.00%)
FOLD 1 Number of neighbors 261
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.96 0.97 0.97 76
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 261
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 261
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 2 52]]
FOLD 4 Number of neighbors 261
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.99 0.97 0.98 75
technology
1.00 0.98 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 261
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 261
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.98 0.93 0.96 70
sport
0.96 1.00 0.98 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 6 ):
[[65 1 4]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 261
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 261 neighbours): 97.02% (+/- 1.00%)
FOLD 1 Number of neighbors 263
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.96 0.99 0.97 76
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 263
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 263
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 263
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.97 0.98 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 263
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 263
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.95 1.00 0.97 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 3 53]]
FOLD 7 Number of neighbors 263
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 263 neighbours): 97.02% (+/- 1.00%)
FOLD 1 Number of neighbors 265
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.96 0.97 0.97 76
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 265
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 265
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.93 0.95 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 265
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.99 0.97 0.98 75
technology
1.00 0.98 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 265
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 265
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.96 1.00 0.98 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 2 54]]
FOLD 7 Number of neighbors 265
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 265 neighbours): 97.02% (+/- 1.00%)
FOLD 1 Number of neighbors 267
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.96 0.99 0.97 76
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 267
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 267
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 267
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.97 0.98 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 267
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 267
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.97 1.00 0.99 75
technology
0.93 0.96 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 0 4]
[ 0 75 0]
[ 0 2 54]]
FOLD 7 Number of neighbors 267
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 267 neighbours): 97.02% (+/- 1.00%)
FOLD 1 Number of neighbors 269
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.96 0.97 0.97 76
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 269
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 269
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 1 74 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 269
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
1.00 0.97 0.99 75
technology
1.00 1.00 1.00 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 269
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 269
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.93 0.96 70
sport
0.96 1.00 0.98 75
technology
0.93 0.96 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[65 1 4]
[ 0 75 0]
[ 0 2 54]]
FOLD 7 Number of neighbors 269
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.97 0.97 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 2 73 0]
[ 1 2 52]]
Model accuracy (for 269 neighbours): 97.02% (+/- 1.00%)
FOLD 1 Number of neighbors 271
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.96 0.97 0.97 76
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 271
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 271
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.95 1.00 0.97 75
technology
0.94 0.91 0.93 56
avg / total 0.96 0.96 0.95 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 3 51]]
FOLD 4 Number of neighbors 271
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 1.00 0.99 70
sport
0.99 0.99 0.99 75
technology
1.00 0.98 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 271
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 271
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.96 0.98 70
sport
0.97 1.00 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 0 2 54]]
FOLD 7 Number of neighbors 271
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 271 neighbours): 97.02% (+/- 1.00%)
FOLD 1 Number of neighbors 273
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.96 0.97 0.97 76
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 273
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 273
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 1 74 0]
[ 2 2 52]]
FOLD 4 Number of neighbors 273
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 1.00 0.99 70
sport
0.99 0.99 0.99 75
technology
1.00 0.98 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 273
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 273
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.93 0.96 70
sport
0.97 1.00 0.99 75
technology
0.92 0.96 0.94 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[65 0 5]
[ 0 75 0]
[ 0 2 54]]
FOLD 7 Number of neighbors 273
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 273 neighbours): 97.02% (+/- 1.00%)
FOLD 1 Number of neighbors 275
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.96 0.97 0.97 76
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 275
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 275
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 275
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.97 0.98 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 275
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 275
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.96 0.98 70
sport
0.97 1.00 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 0 2 54]]
FOLD 7 Number of neighbors 275
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.97 0.97 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 2 73 0]
[ 1 2 52]]
Model accuracy (for 275 neighbours): 97.02% (+/- 1.00%)
FOLD 1 Number of neighbors 277
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.97 0.97 0.97 76
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 277
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 277
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 2 52]]
FOLD 4 Number of neighbors 277
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 1.00 0.99 70
sport
0.99 0.99 0.99 75
technology
1.00 0.98 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 277
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 277
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.96 1.00 0.98 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 2 54]]
FOLD 7 Number of neighbors 277
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.97 0.97 0.97 75
technology
0.93 0.95 0.94 55
avg / total 0.96 0.95 0.96 200
Confusion Matrix (Fold 7 ):
[[66 0 4]
[ 2 73 0]
[ 1 2 52]]
Model accuracy (for 277 neighbours): 97.02% (+/- 1.01%)
FOLD 1 Number of neighbors 279
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.97 0.97 0.97 76
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 279
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.95 0.99 0.97 70
sport
0.97 0.96 0.97 75
technology
0.98 0.95 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 2 ):
[[69 0 1]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 279
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 279
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.97 0.98 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 279
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 279
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.95 1.00 0.97 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 3 53]]
FOLD 7 Number of neighbors 279
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.97 0.97 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 2 73 0]
[ 1 2 52]]
Model accuracy (for 279 neighbours): 97.02% (+/- 1.00%)
FOLD 1 Number of neighbors 281
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.95 0.97 0.96 76
technology
0.96 0.93 0.95 56
avg / total 0.96 0.96 0.96 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 4 52]]
FOLD 2 Number of neighbors 281
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 281
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 281
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.99 0.97 0.98 75
technology
1.00 0.98 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 281
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 281
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.96 0.98 70
sport
0.96 1.00 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 0 3 53]]
FOLD 7 Number of neighbors 281
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 281 neighbours): 97.02% (+/- 1.00%)
FOLD 1 Number of neighbors 283
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.96 0.99 0.97 76
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 75 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 283
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 283
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.96 0.93 0.94 70
sport
0.96 0.99 0.97 75
technology
0.93 0.93 0.93 56
avg / total 0.95 0.95 0.95 201
Confusion Matrix (Fold 3 ):
[[65 1 4]
[ 1 74 0]
[ 2 2 52]]
FOLD 4 Number of neighbors 283
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.97 0.98 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 283
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 283
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.96 0.97 70
sport
0.96 1.00 0.98 75
technology
0.95 0.93 0.94 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 1 3 52]]
FOLD 7 Number of neighbors 283
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.96 0.96 70
sport
0.97 0.97 0.97 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 2 73 0]
[ 1 2 52]]
Model accuracy (for 283 neighbours): 97.02% (+/- 1.01%)
FOLD 1 Number of neighbors 285
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.96 0.97 0.97 76
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 285
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 285
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.96 0.99 0.97 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 1 74 0]
[ 2 2 52]]
FOLD 4 Number of neighbors 285
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
1.00 0.97 0.99 75
technology
1.00 1.00 1.00 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 285
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
1.00 0.99 0.99 75
technology
0.96 0.98 0.97 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 1 0 55]]
FOLD 6 Number of neighbors 285
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.93 0.96 70
sport
0.96 1.00 0.98 75
technology
0.93 0.96 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[65 1 4]
[ 0 75 0]
[ 0 2 54]]
FOLD 7 Number of neighbors 285
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.97 0.97 0.97 75
technology
0.93 0.95 0.94 55
avg / total 0.96 0.95 0.96 200
Confusion Matrix (Fold 7 ):
[[66 0 4]
[ 2 73 0]
[ 1 2 52]]
Model accuracy (for 285 neighbours): 97.02% (+/- 1.01%)
FOLD 1 Number of neighbors 287
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.97 0.99 71
sport
0.96 0.99 0.97 76
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 0 75 1]
[ 0 3 53]]
FOLD 2 Number of neighbors 287
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 287
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.96 1.00 0.98 75
technology
0.95 0.93 0.94 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 2 2 52]]
FOLD 4 Number of neighbors 287
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.99 0.97 0.98 75
technology
1.00 0.98 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 287
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 287
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.93 0.96 70
sport
0.96 1.00 0.98 75
technology
0.93 0.96 0.95 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[65 1 4]
[ 0 75 0]
[ 0 2 54]]
FOLD 7 Number of neighbors 287
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.93 0.95 0.94 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 0 4]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 287 neighbours): 97.02% (+/- 1.01%)
FOLD 1 Number of neighbors 289
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.96 0.97 0.97 76
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 289
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 289
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 1 1 54]]
FOLD 4 Number of neighbors 289
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.97 0.98 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 289
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 289
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.96 1.00 0.98 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 1 3]
[ 0 75 0]
[ 0 2 54]]
FOLD 7 Number of neighbors 289
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.96 0.96 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[67 0 3]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 289 neighbours): 97.02% (+/- 1.01%)
FOLD 1 Number of neighbors 291
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.99 0.97 0.98 71
sport
0.96 0.97 0.97 76
technology
0.95 0.95 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 1 74 1]
[ 0 3 53]]
FOLD 2 Number of neighbors 291
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 291
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 1 74 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 291
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 0.97 0.98 75
technology
0.98 0.98 0.98 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 4 ):
[[69 0 1]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 291
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 291
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.93 0.95 0.94 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 0 4]
[ 0 75 0]
[ 1 2 53]]
FOLD 7 Number of neighbors 291
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 0.99 0.98 75
technology
0.93 0.95 0.94 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 0 4]
[ 1 74 0]
[ 1 2 52]]
Model accuracy (for 291 neighbours): 97.02% (+/- 1.01%)
FOLD 1 Number of neighbors 293
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.96 0.97 0.97 76
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 293
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 293
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 1 74 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 293
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
0.99 0.97 0.98 75
technology
1.00 0.98 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 2 73 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 293
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 293
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.96 0.98 70
sport
0.97 1.00 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 0 2 54]]
FOLD 7 Number of neighbors 293
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.97 0.97 0.97 75
technology
0.93 0.95 0.94 55
avg / total 0.96 0.95 0.96 200
Confusion Matrix (Fold 7 ):
[[66 0 4]
[ 2 73 0]
[ 1 2 52]]
Model accuracy (for 293 neighbours): 97.02% (+/- 1.01%)
FOLD 1 Number of neighbors 295
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.96 0.97 0.97 76
technology
0.96 0.95 0.95 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 3 53]]
FOLD 2 Number of neighbors 295
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 295
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.99 0.94 0.96 70
sport
0.97 1.00 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 0 75 0]
[ 1 1 54]]
FOLD 4 Number of neighbors 295
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 1.00 0.99 70
sport
0.99 0.99 0.99 75
technology
1.00 0.98 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 0 1 55]]
FOLD 5 Number of neighbors 295
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 295
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.96 0.98 70
sport
0.97 1.00 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 0 2 54]]
FOLD 7 Number of neighbors 295
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.97 0.97 0.97 75
technology
0.93 0.95 0.94 55
avg / total 0.96 0.95 0.96 200
Confusion Matrix (Fold 7 ):
[[66 0 4]
[ 2 73 0]
[ 1 2 52]]
Model accuracy (for 295 neighbours): 97.02% (+/- 1.01%)
FOLD 1 Number of neighbors 297
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.97 0.97 0.97 76
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 297
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 297
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 1 74 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 297
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.99 1.00 0.99 70
sport
1.00 0.99 0.99 75
technology
1.00 1.00 1.00 56
avg / total 1.00 1.00 1.00 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 1 74 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 297
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 297
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.96 0.98 70
sport
0.97 1.00 0.99 75
technology
0.95 0.96 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[67 0 3]
[ 0 75 0]
[ 0 2 54]]
FOLD 7 Number of neighbors 297
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.94 0.96 70
sport
0.97 0.97 0.97 75
technology
0.93 0.96 0.95 55
avg / total 0.96 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[66 0 4]
[ 2 73 0]
[ 0 2 53]]
Model accuracy (for 297 neighbours): 97.02% (+/- 1.01%)
FOLD 1 Number of neighbors 299
Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
0.97 0.97 0.97 71
sport
0.97 0.97 0.97 76
technology
0.96 0.96 0.96 56
avg / total 0.97 0.97 0.97 203
Confusion Matrix (Fold 1 ):
[[69 0 2]
[ 2 74 0]
[ 0 2 54]]
FOLD 2 Number of neighbors 299
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.96 0.97 75
technology
0.96 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 3 72 0]
[ 1 2 53]]
FOLD 3 Number of neighbors 299
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.97 0.99 0.98 75
technology
0.95 0.95 0.95 56
avg / total 0.96 0.96 0.96 201
Confusion Matrix (Fold 3 ):
[[66 1 3]
[ 1 74 0]
[ 2 1 53]]
FOLD 4 Number of neighbors 299
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
1.00 0.97 0.99 75
technology
1.00 1.00 1.00 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 2 73 0]
[ 0 0 56]]
FOLD 5 Number of neighbors 299
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
1.00 0.99 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 1 74 0]
[ 0 0 56]]
FOLD 6 Number of neighbors 299
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
1.00 0.94 0.97 70
sport
0.96 1.00 0.98 75
technology
0.93 0.95 0.94 56
avg / total 0.97 0.97 0.97 201
Confusion Matrix (Fold 6 ):
[[66 0 4]
[ 0 75 0]
[ 0 3 53]]
FOLD 7 Number of neighbors 299
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.94 0.95 70
sport
0.97 0.97 0.97 75
technology
0.93 0.95 0.94 55
avg / total 0.96 0.95 0.96 200
Confusion Matrix (Fold 7 ):
[[66 0 4]
[ 2 73 0]
[ 1 2 52]]
Model accuracy (for 299 neighbours): 97.02% (+/- 1.01%)
In [382]:
# changing to misclassification error
MSE_ngram = [1-x/100 for x in k_model_accuracy_ngram]
index_ngram=MSE_ngram.index(min(MSE_ngram))
optimal_k_ngram = neighbors[index_ngram]
print ("The highest model accuracy",k_model_accuracy_ngram[index_ngram],"is achieved by using optimal number of neighbors %d" % optimal_k_ngram)
# plot misclassification error vs k
plt.plot(neighbors, MSE_ngram)
plt.xlabel('Number of Neighbors K')
plt.ylabel('Misclassification Error')
plt.show()
The highest model accuracy 97.03437253145505 is achieved by using optimal number of neighbors 237
From the misclassification error vs number of neighbours k graphs we can see that for all cases error decreases till around k=200 and then error plateau. At one point, for k above certain point, cross validation errors begin to go up again. The bigger the k the more smoothing takes place and it reduces over -fitting.
In [383]:
print ("\nNot balanced, without ngrams (n>1):\n The highest model accuracy",k_model_accuracy_not_bal[index_not_bal],"is achieved by using optimal number of neighbors %d" % optimal_k_not_bal)
print ("\nNot balanced, with ngrams (ngrams=(1,3)):\nThe highest model accuracy",k_model_accuracy_ngram_not_bal[index_ngram_not_bal],"is achieved by using optimal number of neighbors %d" % optimal_k_ngram_not_bal)
print ("\nBalanced, without ngrams (n>1):\nThe highest model accuracy",k_model_accuracy[index],"is achieved by using optimal number of neighbors %d" % optimal_k)
print ("\nNBalanced, with ngrams (ngrams=(1,3)):\nThe highest model accuracy",k_model_accuracy_ngram[index_ngram],"is achieved by using optimal number of neighbors %d" % optimal_k_ngram)
Not balanced, without ngrams (n>1): The highest model accuracy 96.82067537908272 is achieved by using optimal number of neighbors 97 Not balanced, with ngrams (ngrams=(1,3)): The highest model accuracy 96.80225321345199 is achieved by using optimal number of neighbors 125 Balanced, without ngrams (n>1): The highest model accuracy 97.0565916786394 is achieved by using optimal number of neighbors 205 NBalanced, with ngrams (ngrams=(1,3)): The highest model accuracy 97.03437253145505 is achieved by using optimal number of neighbors 237
Hence, the best kNN model is one that uses balanced data/labels in cobination with earlier mentioned preprocessing steps, without ngrams (only tokens containing one word)
Support Vector Machines¶
Often apply SVMs with a linear kernel to calculate document similarity.
In [366]:
cvscores_SVM_not_bal = []
kfold = StratifiedKFold(n_splits=7, shuffle=True, random_state=seed)
fold=0
model = svm.SVC(kernel='linear', C=1)
for train, test in kfold.split(X_term_weighting, class_labels):
fold+=1
print('FOLD',fold)
labels_train=[]
for i in range(len(train)):
labels_train.append(class_labels[train[i]])
labels_test=[]
for i in range(len(test)):
labels_test.append(class_labels[test[i]])
# Fit/Train the model
model.fit(X_term_weighting[train], labels_train)
#Evaluate the Model; Use the test dataset to evaluate the model
print('\n\n ****** Test Data ******** (Fold',fold,'):')
predicted = model.predict(X_term_weighting[test])
# Print performance details
print(metrics.classification_report(labels_test, predicted))
# Print confusion matrix
print('Confusion Matrix (Fold',fold,'):')
print(metrics.confusion_matrix(labels_test, predicted))
cvscores_SVM_not_bal.append(accuracy_score(labels_test, predicted) * 100)
print("\n\n Model accuracy (over all",fold," folds): %.2f%% (+/- %.2f%%)" % (numpy.mean(cvscores_SVM_not_bal), numpy.std(cvscores_SVM_not_bal)))
FOLD 1
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
1.00 1.00 1.00 76
technology
0.98 1.00 0.99 56
avg / total 1.00 1.00 1.00 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 0 56]]
FOLD 2
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
0.99 0.97 0.98 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 1 73 1]
[ 0 1 55]]
FOLD 3
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
1.00 0.97 0.99 70
sport
0.99 1.00 0.99 75
technology
0.98 1.00 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 0 0 56]]
FOLD 4
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
1.00 0.97 0.99 75
technology
1.00 1.00 1.00 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 2 73 0]
[ 0 0 56]]
FOLD 5
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
1.00 0.99 0.99 70
sport
1.00 1.00 1.00 75
technology
0.98 1.00 0.99 56
avg / total 1.00 1.00 1.00 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 0 75 0]
[ 0 0 56]]
FOLD 6
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.97 1.00 0.99 75
technology
0.98 0.95 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[68 1 1]
[ 0 75 0]
[ 2 1 53]]
FOLD 7
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.97 0.99 0.98 75
technology
0.98 0.96 0.97 55
avg / total 0.98 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[68 1 1]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (over all 7 folds): 98.51% (+/- 0.88%)
In [367]:
cvscores_SVM_ngram_not_bal = []
kfold = StratifiedKFold(n_splits=7, shuffle=True, random_state=seed)
fold=0
model = svm.SVC(kernel='linear', C=1)
for train, test in kfold.split(X_term_weighting_ngram, class_labels):
fold+=1
print('FOLD',fold)
labels_train=[]
for i in range(len(train)):
labels_train.append(class_labels[train[i]])
labels_test=[]
for i in range(len(test)):
labels_test.append(class_labels[test[i]])
# Fit/Train the model
model.fit(X_term_weighting_ngram[train], labels_train)
#Evaluate the Model; Use the test dataset to evaluate the model
print('\n\n ****** Test Data ******** (Fold',fold,'):')
predicted = model.predict(X_term_weighting_ngram[test])
# Print performance details
print(metrics.classification_report(labels_test, predicted))
# Print confusion matrix
print('Confusion Matrix (Fold',fold,'):')
print(metrics.confusion_matrix(labels_test, predicted))
cvscores_SVM_ngram_not_bal.append(accuracy_score(labels_test, predicted) * 100)
print("\n\n Model accuracy (over all",fold," folds): %.2f%% (+/- %.2f%%)" % (numpy.mean(cvscores_SVM_ngram_not_bal), numpy.std(cvscores_SVM_ngram_not_bal)))
FOLD 1
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
1.00 1.00 1.00 76
technology
0.98 1.00 0.99 56
avg / total 1.00 1.00 1.00 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 0 56]]
FOLD 2
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
0.99 0.97 0.98 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 1 73 1]
[ 0 1 55]]
FOLD 3
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
1.00 0.97 0.99 70
sport
0.99 1.00 0.99 75
technology
0.98 1.00 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 3 ):
[[68 1 1]
[ 0 75 0]
[ 0 0 56]]
FOLD 4
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
1.00 0.97 0.99 75
technology
1.00 1.00 1.00 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 2 73 0]
[ 0 0 56]]
FOLD 5
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
1.00 0.99 0.99 70
sport
1.00 1.00 1.00 75
technology
0.98 1.00 0.99 56
avg / total 1.00 1.00 1.00 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 0 75 0]
[ 0 0 56]]
FOLD 6
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.97 1.00 0.99 75
technology
0.98 0.95 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[68 1 1]
[ 0 75 0]
[ 2 1 53]]
FOLD 7
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.94 0.97 0.96 70
sport
0.97 0.99 0.98 75
technology
0.98 0.93 0.95 55
avg / total 0.97 0.96 0.96 200
Confusion Matrix (Fold 7 ):
[[68 1 1]
[ 1 74 0]
[ 3 1 51]]
Model accuracy (over all 7 folds): 98.36% (+/- 1.09%)
In [372]:
cvscores_SVM = []
kfold = StratifiedKFold(n_splits=7, shuffle=True, random_state=seed)
fold=0
model = svm.SVC(kernel='linear', C=1)
for train, test in kfold.split(X_term_weighting, class_labels):
fold+=1
print('FOLD',fold)
labels_train=[]
for i in range(len(train)):
labels_train.append(class_labels[train[i]])
labels_test=[]
for i in range(len(test)):
labels_test.append(class_labels[test[i]])
# Plot a bar plot of the labels: class distribution is adjusted
#seaborn.countplot - Show value counts for a single categorical variable:
print('Class Distribution --> Train data (Fold',fold,'):')
ax = sns.countplot(labels_train)
ax.set_title("Distribution of the Labels (without N/A)")
plt.show()
# Apply the random under-sampling
#Oversampling and undersampling in data analysis are techniques used to adjust the class distribution of a data set
#(i.e. the ratio between the different classes/categories represented).
rus = RandomUnderSampler(return_indices=True)
train_rus, train_labels_rus, idx_resampled = rus.fit_sample(X_term_weighting[train], labels_train)
train_rus, train_labels_rus = shuffle(train_rus, train_labels_rus)
# Plot a bar plot of the labels
#seaborn.countplot - Show value counts for a single categorical variable:
print('Class Distribution after performing under-sampling --> Train data (Fold',fold,'):')
ax = sns.countplot(train_labels_rus)
sns.countplot(train_labels_rus) #--> class distribution is adjusted
plt.show()
# Fit/Train the model
model.fit(train_rus, train_labels_rus)
#Evaluate the Model; Use the test dataset to evaluate the model
print('\n\n ****** Test Data ******** (Fold',fold,'):')
predicted = model.predict(X_term_weighting[test])
# Print performance details
print(metrics.classification_report(labels_test, predicted))
# Print confusion matrix
print('Confusion Matrix (Fold',fold,'):')
print(metrics.confusion_matrix(labels_test, predicted))
cvscores_SVM.append(accuracy_score(labels_test, predicted) * 100)
print("\n\n Model accuracy (over all",fold," folds): %.2f%% (+/- %.2f%%)" % (numpy.mean(cvscores_SVM), numpy.std(cvscores_SVM)))
FOLD 1 Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
1.00 1.00 1.00 76
technology
0.98 1.00 0.99 56
avg / total 1.00 1.00 1.00 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 0 56]]
FOLD 2
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
0.99 0.97 0.98 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 1 73 1]
[ 0 1 55]]
FOLD 3
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
1.00 0.96 0.98 70
sport
0.99 1.00 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 0 0 56]]
FOLD 4
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
1.00 0.97 0.99 75
technology
1.00 1.00 1.00 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 2 73 0]
[ 0 0 56]]
FOLD 5
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
1.00 0.97 0.99 70
sport
1.00 1.00 1.00 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[68 0 2]
[ 0 75 0]
[ 0 0 56]]
FOLD 6
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.99 0.99 0.99 70
sport
0.99 1.00 0.99 75
technology
0.98 0.96 0.97 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 6 ):
[[69 0 1]
[ 0 75 0]
[ 1 1 54]]
FOLD 7
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.97 0.97 0.97 70
sport
0.97 0.99 0.98 75
technology
0.98 0.96 0.97 55
avg / total 0.98 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[68 1 1]
[ 1 74 0]
[ 1 1 53]]
Model accuracy (over all 7 folds): 98.51% (+/- 0.71%)
In [387]:
cvscores_SVM_ngram = []
kfold = StratifiedKFold(n_splits=7, shuffle=True, random_state=seed)
fold=0
model = svm.SVC(kernel='linear', C=1)
for train, test in kfold.split(X_term_weighting_ngram, class_labels):
fold+=1
print('FOLD',fold)
labels_train=[]
for i in range(len(train)):
labels_train.append(class_labels[train[i]])
labels_test=[]
for i in range(len(test)):
labels_test.append(class_labels[test[i]])
# Plot a bar plot of the labels: class distribution is adjusted
#seaborn.countplot - Show value counts for a single categorical variable:
print('Class Distribution --> Train data (Fold',fold,'):')
ax = sns.countplot(labels_train)
ax.set_title("Distribution of the Labels (without N/A)")
plt.show()
# Apply the random under-sampling
#Oversampling and undersampling in data analysis are techniques used to adjust the class distribution of a data set
#(i.e. the ratio between the different classes/categories represented).
rus = RandomUnderSampler(return_indices=True)
train_rus, train_labels_rus, idx_resampled = rus.fit_sample(X_term_weighting_ngram[train], labels_train)
train_rus, train_labels_rus = shuffle(train_rus, train_labels_rus)
# Plot a bar plot of the labels
#seaborn.countplot - Show value counts for a single categorical variable:
print('Class Distribution after performing under-sampling --> Train data (Fold',fold,'):')
ax = sns.countplot(train_labels_rus)
sns.countplot(train_labels_rus) #--> class distribution is adjusted
plt.show()
# Fit/Train the model
model.fit(train_rus, train_labels_rus)
#Evaluate the Model; Use the test dataset to evaluate the model
print('\n\n ****** Test Data ******** (Fold',fold,'):')
predicted = model.predict(X_term_weighting_ngram[test])
# Print performance details
print(metrics.classification_report(labels_test, predicted))
# Print confusion matrix
print('Confusion Matrix (Fold',fold,'):')
print(metrics.confusion_matrix(labels_test, predicted))
cvscores_SVM_ngram.append(accuracy_score(labels_test, predicted) * 100)
print("\n\n Model accuracy (over all",fold," folds): %.2f%% (+/- %.2f%%)" % (numpy.mean(cvscores_SVM_ngram), numpy.std(cvscores_SVM_ngram)))
FOLD 1 Class Distribution --> Train data (Fold 1 ):
Class Distribution after performing under-sampling --> Train data (Fold 1 ):
****** Test Data ******** (Fold 1 ):
precision recall f1-score support
business
1.00 0.99 0.99 71
sport
1.00 1.00 1.00 76
technology
0.98 1.00 0.99 56
avg / total 1.00 1.00 1.00 203
Confusion Matrix (Fold 1 ):
[[70 0 1]
[ 0 76 0]
[ 0 0 56]]
FOLD 2
Class Distribution --> Train data (Fold 2 ):
Class Distribution after performing under-sampling --> Train data (Fold 2 ):
****** Test Data ******** (Fold 2 ):
precision recall f1-score support
business
0.99 0.97 0.98 70
sport
0.99 0.97 0.98 75
technology
0.95 0.98 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 2 ):
[[68 0 2]
[ 1 73 1]
[ 0 1 55]]
FOLD 3
Class Distribution --> Train data (Fold 3 ):
Class Distribution after performing under-sampling --> Train data (Fold 3 ):
****** Test Data ******** (Fold 3 ):
precision recall f1-score support
business
1.00 0.96 0.98 70
sport
0.99 1.00 0.99 75
technology
0.97 1.00 0.98 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 3 ):
[[67 1 2]
[ 0 75 0]
[ 0 0 56]]
FOLD 4
Class Distribution --> Train data (Fold 4 ):
Class Distribution after performing under-sampling --> Train data (Fold 4 ):
****** Test Data ******** (Fold 4 ):
precision recall f1-score support
business
0.97 1.00 0.99 70
sport
1.00 0.97 0.99 75
technology
1.00 1.00 1.00 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 4 ):
[[70 0 0]
[ 2 73 0]
[ 0 0 56]]
FOLD 5
Class Distribution --> Train data (Fold 5 ):
Class Distribution after performing under-sampling --> Train data (Fold 5 ):
****** Test Data ******** (Fold 5 ):
precision recall f1-score support
business
0.99 0.99 0.99 70
sport
1.00 0.99 0.99 75
technology
0.98 1.00 0.99 56
avg / total 0.99 0.99 0.99 201
Confusion Matrix (Fold 5 ):
[[69 0 1]
[ 1 74 0]
[ 0 0 56]]
FOLD 6
Class Distribution --> Train data (Fold 6 ):
Class Distribution after performing under-sampling --> Train data (Fold 6 ):
****** Test Data ******** (Fold 6 ):
precision recall f1-score support
business
0.97 0.99 0.98 70
sport
0.99 1.00 0.99 75
technology
0.98 0.95 0.96 56
avg / total 0.98 0.98 0.98 201
Confusion Matrix (Fold 6 ):
[[69 0 1]
[ 0 75 0]
[ 2 1 53]]
FOLD 7
Class Distribution --> Train data (Fold 7 ):
Class Distribution after performing under-sampling --> Train data (Fold 7 ):
****** Test Data ******** (Fold 7 ):
precision recall f1-score support
business
0.96 0.97 0.96 70
sport
0.97 0.99 0.98 75
technology
0.98 0.95 0.96 55
avg / total 0.97 0.97 0.97 200
Confusion Matrix (Fold 7 ):
[[68 1 1]
[ 1 74 0]
[ 2 1 52]]
Model accuracy (over all 7 folds): 98.36% (+/- 0.83%)
k-NN Results¶
In [392]:
print ("\nNot balanced, without ngrams (n>1):\n The highest model accuracy",k_model_accuracy_not_bal[index_not_bal],"is achieved by using optimal number of neighbors %d" % optimal_k_not_bal)
print ("\nNot balanced, with ngrams (ngrams=(1,3)):\nThe highest model accuracy",k_model_accuracy_ngram_not_bal[index_ngram_not_bal],"is achieved by using optimal number of neighbors %d" % optimal_k_ngram_not_bal)
print ("\nBalanced, without ngrams (n>1):\nThe highest model accuracy",k_model_accuracy[index],"is achieved by using optimal number of neighbors %d" % optimal_k)
print ("\nBalanced, with ngrams (ngrams=(1,3)):\nThe highest model accuracy",k_model_accuracy_ngram[index_ngram],"is achieved by using optimal number of neighbors %d" % optimal_k_ngram)
Not balanced, without ngrams (n>1): The highest model accuracy 96.82067537908272 is achieved by using optimal number of neighbors 97 Not balanced, with ngrams (ngrams=(1,3)): The highest model accuracy 96.80225321345199 is achieved by using optimal number of neighbors 125 Balanced, without ngrams (n>1): The highest model accuracy 97.0565916786394 is achieved by using optimal number of neighbors 205 Balanced, with ngrams (ngrams=(1,3)): The highest model accuracy 97.03437253145505 is achieved by using optimal number of neighbors 237
SVM Results¶
In [393]:
print("\nNot balanced, without ngrams (n>1):\n Model accuracy (over all",fold," folds): %.2f%% (+/- %.2f%%)" % (numpy.mean(cvscores_SVM_not_bal), numpy.std(cvscores_SVM_not_bal)))
print("\nNot balanced, with ngrams (ngrams=(1,3)):\n Model accuracy (over all",fold," folds): %.2f%% (+/- %.2f%%)" % (numpy.mean(cvscores_SVM_ngram_not_bal), numpy.std(cvscores_SVM_ngram_not_bal)))
print("\nBalanced, without ngrams (n>1):\n Model accuracy (over all",fold," folds): %.2f%% (+/- %.2f%%)" % (numpy.mean(cvscores_SVM), numpy.std(cvscores_SVM)))
print("\nBalanced, with ngrams (ngrams=(1,3)):\n Model accuracy (over all",fold," folds): %.2f%% (+/- %.2f%%)" % (numpy.mean(cvscores_SVM_ngram), numpy.std(cvscores_SVM_ngram)))
Not balanced, without ngrams (n>1): Model accuracy (over all 7 folds): 98.51% (+/- 0.88%) Not balanced, with ngrams (ngrams=(1,3)): Model accuracy (over all 7 folds): 98.36% (+/- 1.09%) Balanced, without ngrams (n>1): Model accuracy (over all 7 folds): 98.51% (+/- 0.71%) Balanced, with ngrams (ngrams=(1,3)): Model accuracy (over all 7 folds): 98.36% (+/- 0.83%)
Best kNN accuracy: 97.06%¶
Best SVM accuracy: 98.51%¶
We can see that the SVM performs a bit better than kNN. Also, the best accuracy for both algorithms was achieved when using balanced distribution, and following preprocessing steps: filtering out english stop words, filtering out terms that appear less than 5 times, reducing all the terms to its canonical form (lemmatization). Also all words are lower case and more weights are given to the more "important" terms.
Also by using three-grams we are getting high accuracy as well and with that we are solving the problem of losing the order og words in a sentence (2nd best kNN accuracy: 97.03%, 2nd best SVM accuracy: 98.36%)